Multi-Label Learning with Millions of Labels for Query Recommendation

Size: px
Start display at page:

Download "Multi-Label Learning with Millions of Labels for Query Recommendation"

Transcription

1 Multi-Label Learning with Millions of Labels for Query Recommendation Rahul Agrawal Microsoft AdCenter Yashoteja Prabhu Microsoft Research India Archit Gupta IIT Delhi Manik Varma Microsoft Research India

2 Recommending Advertiser Bid Phrases geico auto insurance geico car insurance geico insurance www geico com care geicos geico com need cheap auto insurance wisconsin cheap car insurance quotes cheap auto insurance florida all state car insurance coupon code

3 Query Rewriting geico auto insurance geico car insurance Absolutely cheapest car insurance geico insurance www geico com care geicos geico com need cheap auto insurance wisconsin cheap car insurance quotes cheap auto insurance florida all state car insurance coupon code

4 Ranking & Relevance Meta Stream geico auto insurance geico car insurance geico insurance www geico com care geicos geico com need cheap auto insurance wisconsin cheap car insurance quotes cheap auto insurance florida geico twitter

5 Recommending Advertiser Bid Phrases geico auto insurance geico car insurance geico insurance www geico com care geicos geico com need cheap auto insurance wisconsin cheap car insurance quotes cheap auto insurance florida all state car insurance coupon code

6 Learning to Predict a Set of Queries italian restaurant f : X 2 Y need cheap auto insurance geico online quote car insurance iphone X: Ads Y: Queries

7 Learning to Predict a Set of Queries f ( ) need cheap auto insurance geico car insurance

8 Multi-Label Learning Challenges f ( ) need cheap auto insurance geico car insurance Infinite number of labels (queries) Training data acquisition Efficient training Cost of prediction

9 Binary Classification & Ranking h : (X, Y) {, } h(, geico) h(, iphone) Infinite number of labels (queries) Training data acquisition Efficient training Cost of prediction

10 Binary Classification h : (X, Y) {, } italian restaurant need cheap auto insurance geico online quote car insurance Infinite number of labels (queries) Training data acquisition Efficient training Cost of prediction iphone

11 Binary Classification KEX h : (X, Y) {, } switching to geico geico online quote car insurance Infinite number of labels (queries) Training data acquisition Efficient training Cost of prediction

12 Query Recommendations by KEX

13 Query Recommendations by KEX h(, car insurance)? h(, iphone)?

14 Query Recommendations by KEX plastic ponies simone plastics clothing and accessories sylvia pony clothing couture playground plastic recycling children's clothing

15 Multi-Label Learning Formulation italian restaurant f : X 2 Y need cheap auto insurance geico online quote car insurance iphone X: Ads Y: Queries

16 Learning with Millions of Labels italian restaurant f : X 2 Y need cheap auto insurance geico online quote car insurance iphone X: Ads Y: 10 Million Queries

17 Multi-Label Random Forests We develop Multi-Label Random Forests with logarithmic prediction costs that make predictions in a few milliseconds. We train on 200 M points, 100 M categories and 10 M features in 28 hours on a grid with 1000 compute nodes. We develop a tree growing criterion which learns from positive data alone. We generate training data automatically from click logs. We develop a sparse SSL formulation to infer beliefs about the state of missing and noisy labels.

18 Training Data Missing Labels No annotator can mark all the relevant labels for a data point. We have missing labels during Training Validation Testing. Even fundamental ML techniques such as validation can go awry. One can t design error metrics invariant to missing labels.

19 Training Data and Features iphone color material TF-IDF Bag of Words Features

20 Training Labels case for iphone best iphone case apple iphone 3g metallic slim fit case best iphone nn4 cases iphone cases best iphone cases apple iphone 4g cases best iphone nn4 case iphone 3gs cases iphone 4s case case iphone otterbox universal defender case iphone nn4 black silicone black plastic sena iphone cases apple iphone 4g premium soft silicone rubber black phone protector skin cover case apple iphone nn4 cases belkin grip vue tint case iphone nn4 clear black white premium bumper case apple iphone nn4 att bunny rabbit silicone case skin iphone nn4 stand tail holder iphone color material iphone case iphone 4g cases iphone case speck iphone case best case iphone 4s iphone 4gs cases iphone nn4 case switcheasy neo case iphone 3g black best case iphone nn4 iphone 4s defender series case 3g iphone cases waterproof iphone case best iphone 3g cases iphone case design TF-IDF Bag of Words Features iphone cases 4g apple iphone cases waterproof iphone cases best iphone 4s case iphone cases 3g best iphone 3g case amazonbasics protective tpu case screen protector att verizon iphone nn4 iphone 4s clear best iphone 4s cases

21 Training Labels

22 Missing and Noisy Labels best italian restaurants philadelphia italian restaurants italian restaurant italian restaurants arkansas italian restaurants connecticut italian restaurants idaho italian restaurants phoenix italian restaurant chains italian restaurant connecticut italian restaurant district columbia thai restaurant thai restaurants restaurants mexican restaurants

23 Missing and Noisy Labels

24 Frequency Biased Training Data Most labels will have very few positive training examples Zipf's Law

25 Multi-Label Prediction Costs Linear prediction costs are infeasible geico car insurance pizza iphone cases 1-vs-All Classification

26 Label and Feature Space Compression 10M Dimensional Label Space car motor vehicle auto iphone cases iphone case cases iphone 6M Dimensional Feature Space Car Ads iphone Case Ads 1K Dimensional Embedding Space

27 Hierarchical Prediction Prediction in logarithmic time

28 Gating Tree Based Prediction Prediction in logarithmic time Is the word insurance present in the ad? Yes No Is the word geico present in the ad? Yes No

29 Ensemble of Randomized Gating Trees

30 Efficient Training We seek classifiers and optimization algorithms that Are massively parallelizable Don t need to load the feature vectors (1 Tb) into RAM Don t need to load the label matrix (100 Gb) into RAM Number of training points Number of labels Dimensionality of feature vector 200 Million 100 Million 10 Million Number of cores RAM per core Training time 2 Gb 28 hours

31 Multi-Label Random Forests The splitting cost needs to be calculated in a 2 10M space Is the word insurance present?

32 Learning from Positively Labeled Data Split condition : x f > t f, t = argmin f,t n l k p l l k (1 p l l k ) + n r k p r l k (1 p r l k ) p l k = i p l k ad i p(ad i ) x f > t l1 l2 l3 0 l1 l2 l3

33 Multi-Label Random Forests x 1, y 1 = {l 2, l 3 } 1 0 l1 l2 l3 (x 1, y 1 ) (x 2, y 2 ) (x 3, y 3 ) x 2, y 2 = {l 1, l 3 } x 3, y 3 = {l 1, l 2, l 3 } l1 l2 l3 l1 l2 l3 p(y) l1 l2 l3

34 Query Recommendation Data Sets Data set statistics Data Set # of Training Points (M) # of Test Points (M) # of Dimensions (M) # of Labels (M) Wikipedia Ads Web Ads

35 Performance Evaluation We use loss functions where the penalty incurred for predicting the real (but unknown) ground truth is never more than that of predicting any other labelling L y, y Observed L y, y Observed y Y Hamming Loss Precision at k We found Precision at 10 to be robust for our application.

36 Query Recommendation Results MLRF KEX Wikipedia Ads1 Web Ads2 Percentage of top 10 predictions that were clicked queries

37 Query Recommendation Results MLRF KEX Wikipedia Ads1 Web Ads2 Percentage of top 10 predictions that were relevant

38

39 Geico Car Insurance KEX MLRF geico auto insurance geico car insurance geico insurance www geico com care geicos geico com need cheap auto insurance wisconsin cheap car insurance quotes cheap auto insurance florida all state car insurance coupon code

40

41 Domino s Pizza KEX MLRF dominos dominos pizza domino pizza domino pasta bowls domino pizza coupons domino pizza deals domino pizza locations domino pizza menu domino pizza online

42

43 Simone & Sylvia Kid s Clothing KEX plastic ponies simone plastics clothing and accessories sylvia pony clothing couture playground Plastic recycling children's clothing MLRF toddlers clothes toddlers clothing toddler costumes children clothes sale children clothes designer children clothes cute children clothes retro clothing retro baby clothes baby clothing

44

45 KCS Flowers KEX funeral flowers sympathy funeral flowers web home bleitz funeral home funeral flowers discount yarington's funeral home harvey funeral home green lake funeral home howden kennedy funeral home arranging flowers MLRF flowers delivery funeral arrangements birthday flowers funeral flowers funeral planning flowers valentines free delivery flowers cheap flowers florists cheap flowers funeral

46

47 Vistaprint Designer T-Shirts KEX embroidered apparel custom apparel readymade apparel customizable apparel customizable apparel leading print online business cards apparel and accessories own text MLRF custom t shirts funny t shirts hanes beefy t shirts hanes t shirts long sleeve t shirts personalized t shirts printed t shirts retro gamer t shirts t shirts buy custom t shirts

48

49 Metlife Auto Insurance KEX metlife auto home insurance auto home insurance auto insurance massachusetts metlife agent driver discount additional cost saving benefits car discount auto quote MLRF metlife auto insurance auto Insurance car Insurance automobile Insurance geico insurance cheap car insurance metlife auto insurance broker insurance home insurance

50

51 Wanta Thai Restaurant KEX authentic thai restaurant delicious thai food thai cuisine thai restaurant thai food wanta best thai restaurant thai eateries thai contemporary thai MLRF thai restaurant thai restaurants mexican restaurants cheap hotels hotels fast food restaurants restaurants coupons best web hosting restaurants vegetarian foods new york restaurants

52

53 best italian restaurants philadelphia italian restaurants italian restaurant italian restaurants arkansas italian restaurants connecticut italian restaurants idaho italian restaurants phoenix italian restaurant chains italian restaurant connecticut italian restaurant district columbia thai restaurant thai restaurants restaurants mexican restaurants

54

55 Compensating for Missing Labels 0.5 Case-mate phone cases 0.7 Auto insurance quotes Esurance 0.8 American family insurance 0.9 Progressive insurance Allstate auto insurance Maggiano s restaurant

56 Training on Belief Vectors 1 x 1, y 1 = l 2, l 3, f 1 0 l1 l2 l3 (x 1, f 1 ) (x 2, f 2 ) (x 3, f 3 ) x 2, y 2 = l 1, l 3, f 2 x 3, y 3 = l 1, l 2, l 3, f l1 l2 l3 l1 l2 l3 1 p(f) l1 l2 l3

57 Sparse Semi-Supervised Learning Graph-based SSL optimizes label belief smoothness and fidelity to original labels 1 F* = Min Tr F 2 Ft I D 1 2 W D 1 2 F + λ 2 s. t. F 0 K F Y 2 W MXM D MXM Y MXL F MXL λ M L K Document-document similarity matrix Diagonal matrix representing the row sums of W 0/1 label matrix Real valued label belief matrix Trade-off Hyperparameter Number of documents Number of labels Sparsity constant

58 Sparse Semi-Supervised Learning Graph-based SSL optimizes label belief smoothness and fidelity to original labels F* = Min F 1 Σ 2 i=1..lσ j=1..m l=1..m s. t. F 0 K w jl ( F ij D jj F il D ll ) 2 + λ 2 Σ i=1..m j=1..l (F ij Y ij ) 2 W MXM D MXM Y MXL F MXL λ M L K Document-document similarity matrix Diagonal matrix representing the row sums of W 0/1 label matrix Real valued label belief matrix Trade-off Hyperparameter Number of documents Number of labels Sparsity constant

59 Iterative Hard Thresholding Sparse SSL formulation F* = Min F J F = 1 2 Tr Ft I D 1 2 W D 1 2 F + λ 2 s. t. F 0 K F Y 2 The iterative hard thresholding algorithm converges to a global/local optimum F 0 F t+ 1 2 = Y = 1 λ+1 D 1 2 W D 1 2F t + F t+1 = Top K (F 1 t+ ) 2 λ λ+1 Y

60 Iterative Hard Thresholding If Y ij {0, 1} and W is positive definite then The sequence F 0, F 1, converges to a stationary point F. J(F 0 ) J(F 1 ) J(F ) If F 0 < K then F is a globally optimal solution If F 0 = K then F is a locally optimal solution J F J F + Min( λ 2 K + Y λ + 1 0, 2 ML K α K (F ) Y 0 )

61 Semi-Supervised Learning Results as judged by automatically generated click labels as well as by human experts. Data Set MLRF Click Labels (%) Human Verification (%) MLRF+ SSL KEX MLRF MLRF+ SSL Wikipedia KEX Ads Bing Ads

62 Query Expansion Results Query expansion techniques can help both KEX and MLRF Data Set Click Labels (%) Human Verification (%) MLRF+ SSL+KSP KEX+KSP MLRF+ SSL+KSP KEX+KSP Wikipedia Ads Web Ads

63 Query Recommendation Results Edit distance [Ravi et al. WSDM 2010] Data Set Click Labels (%) KEX KEX+KSP MLRF MLRF+SSL MLRF+SSL+ KSP Wikipedia Ads Web Ads

64 Conclusions Query recommendation can be posed as multi-label learning. Learning with millions of labels can be tractable and accurate. Other applications Query expansion. Document and ad relevance and ranking. Fine-grained query intent classification.

65 Deepak Bapna Prateek Jain A. Kumaran Mehul Parsana Krishna Leela Poola Adarsh Prasad Varun Singla Acknowledgements

66 Advantages of an ML Approach Can generalize to other domains such as images on Flickr or videos on YouTube.

67 System Architecture We leverage the Map/Reduce framework. Trees are grown in parallel breadth-wise. Number of compute nodes Evaluators 500 Combiners 100 Maximizers 25 Evaluator 1 Maximizer 1 Maximizer 2 Combiner 1 Evaluator 2 F*, T* Combiner 2 Evaluator 3 Combiner 3 Evaluator 4 Our objective is to balance the compute load across machines while minimizing data flow X 1,Y 1 to X N, Y N X N+1,Y N+1 to X 2N, Y 2N X 2N+1,Y 2N+1 to X 3N, Y 3N X 3N+1,Y 3N+1 to X 4N, Y 4N

68 Evaluators Input N training instances Set of keys Tree ID, Node ID, Feature ID and threshold Output Partial label distributions for the keys Evaluator 1 Maximizer 1 Maximizer 2 Combiner 1 Evaluator 2 F*, T* Combiner 2 Evaluator 3 Combiner 3 Evaluator 4 Computation N * # of keys X 1,Y 1 to X N, Y N X N+1,Y N+1 to X 2N, Y 2N X 2N+1,Y 2N+1 to X 3N, Y 3N X 3N+1,Y 3N+1 to X 4N, Y 4N

69 Combiners Input Partial label distributions for assigned keys F*, T* Maximizer 1 Maximizer 2 Output Objective function values for the keys. Combiner 1 Combiner 2 Combiner 3 Computation # of keys * Avg # of Evaluators / key * # of labels in the distribution for the key. X N+1,Y N+1 Evaluator 1 Evaluator 2 Evaluator 3 Evaluator 4 X 1,Y 1 to X N, Y N to X 2N, Y 2N X 2N+1,Y 2N+1 to X 3N, Y 3N X 3N+1,Y 3N+1 to X 4N, Y 4N

70 Maximizers Input Objective function values for assigned keys Output Optimal feature and threshold for assigned nodes in trees. Computation # of keys * Avg # of features per key * Avg # of Evaluator 1 Maximizer 1 Maximizer 2 Combiner 1 Evaluator 2 F*, T* Combiner 2 Evaluator 3 Combiner 3 Evaluator 4 thresholds per feature X 1,Y 1 X N+1,Y N+1 to X N, Y N to X 2N, Y 2N X 2N+1,Y 2N+1 to X 3N, Y 3N X 3N+1,Y 3N+1 to X 4N, Y 4N

Non-negative Matrix Factorization (NMF) in Semi-supervised Learning Reducing Dimension and Maintaining Meaning

Non-negative Matrix Factorization (NMF) in Semi-supervised Learning Reducing Dimension and Maintaining Meaning Non-negative Matrix Factorization (NMF) in Semi-supervised Learning Reducing Dimension and Maintaining Meaning SAMSI 10 May 2013 Outline Introduction to NMF Applications Motivations NMF as a middle step

More information

LABEL PROPAGATION ON GRAPHS. SEMI-SUPERVISED LEARNING. ----Changsheng Liu 10-30-2014

LABEL PROPAGATION ON GRAPHS. SEMI-SUPERVISED LEARNING. ----Changsheng Liu 10-30-2014 LABEL PROPAGATION ON GRAPHS. SEMI-SUPERVISED LEARNING ----Changsheng Liu 10-30-2014 Agenda Semi Supervised Learning Topics in Semi Supervised Learning Label Propagation Local and global consistency Graph

More information

Data Mining Practical Machine Learning Tools and Techniques

Data Mining Practical Machine Learning Tools and Techniques Ensemble learning Data Mining Practical Machine Learning Tools and Techniques Slides for Chapter 8 of Data Mining by I. H. Witten, E. Frank and M. A. Hall Combining multiple models Bagging The basic idea

More information

Hadoop Usage At Yahoo! Milind Bhandarkar (milindb@yahoo-inc.com)

Hadoop Usage At Yahoo! Milind Bhandarkar (milindb@yahoo-inc.com) Hadoop Usage At Yahoo! Milind Bhandarkar (milindb@yahoo-inc.com) About Me Parallel Programming since 1989 High-Performance Scientific Computing 1989-2005, Data-Intensive Computing 2005 -... Hadoop Solutions

More information

Search Engines. Stephen Shaw 18th of February, 2014. Netsoc

Search Engines. Stephen Shaw <stesh@netsoc.tcd.ie> 18th of February, 2014. Netsoc Search Engines Stephen Shaw Netsoc 18th of February, 2014 Me M.Sc. Artificial Intelligence, University of Edinburgh Would recommend B.A. (Mod.) Computer Science, Linguistics, French,

More information

Similarity Search in a Very Large Scale Using Hadoop and HBase

Similarity Search in a Very Large Scale Using Hadoop and HBase Similarity Search in a Very Large Scale Using Hadoop and HBase Stanislav Barton, Vlastislav Dohnal, Philippe Rigaux LAMSADE - Universite Paris Dauphine, France Internet Memory Foundation, Paris, France

More information

Machine Learning Final Project Spam Email Filtering

Machine Learning Final Project Spam Email Filtering Machine Learning Final Project Spam Email Filtering March 2013 Shahar Yifrah Guy Lev Table of Content 1. OVERVIEW... 3 2. DATASET... 3 2.1 SOURCE... 3 2.2 CREATION OF TRAINING AND TEST SETS... 4 2.3 FEATURE

More information

Journée Thématique Big Data 13/03/2015

Journée Thématique Big Data 13/03/2015 Journée Thématique Big Data 13/03/2015 1 Agenda About Flaminem What Do We Want To Predict? What Is The Machine Learning Theory Behind It? How Does It Work In Practice? What Is Happening When Data Gets

More information

Social Media Mining. Data Mining Essentials

Social Media Mining. Data Mining Essentials Introduction Data production rate has been increased dramatically (Big Data) and we are able store much more data than before E.g., purchase data, social media data, mobile phone data Businesses and customers

More information

Hadoop SNS. renren.com. Saturday, December 3, 11

Hadoop SNS. renren.com. Saturday, December 3, 11 Hadoop SNS renren.com Saturday, December 3, 11 2.2 190 40 Saturday, December 3, 11 Saturday, December 3, 11 Saturday, December 3, 11 Saturday, December 3, 11 Saturday, December 3, 11 Saturday, December

More information

Collective Behavior Prediction in Social Media. Lei Tang Data Mining & Machine Learning Group Arizona State University

Collective Behavior Prediction in Social Media. Lei Tang Data Mining & Machine Learning Group Arizona State University Collective Behavior Prediction in Social Media Lei Tang Data Mining & Machine Learning Group Arizona State University Social Media Landscape Social Network Content Sharing Social Media Blogs Wiki Forum

More information

The Impact of Big Data on Classic Machine Learning Algorithms. Thomas Jensen, Senior Business Analyst @ Expedia

The Impact of Big Data on Classic Machine Learning Algorithms. Thomas Jensen, Senior Business Analyst @ Expedia The Impact of Big Data on Classic Machine Learning Algorithms Thomas Jensen, Senior Business Analyst @ Expedia Who am I? Senior Business Analyst @ Expedia Working within the competitive intelligence unit

More information

Big Data Text Mining and Visualization. Anton Heijs

Big Data Text Mining and Visualization. Anton Heijs Copyright 2007 by Treparel Information Solutions BV. This report nor any part of it may be copied, circulated, quoted without prior written approval from Treparel7 Treparel Information Solutions BV Delftechpark

More information

Ensemble Methods. Knowledge Discovery and Data Mining 2 (VU) (707.004) Roman Kern. KTI, TU Graz 2015-03-05

Ensemble Methods. Knowledge Discovery and Data Mining 2 (VU) (707.004) Roman Kern. KTI, TU Graz 2015-03-05 Ensemble Methods Knowledge Discovery and Data Mining 2 (VU) (707004) Roman Kern KTI, TU Graz 2015-03-05 Roman Kern (KTI, TU Graz) Ensemble Methods 2015-03-05 1 / 38 Outline 1 Introduction 2 Classification

More information

Attribution. Modified from Stuart Russell s slides (Berkeley) Parts of the slides are inspired by Dan Klein s lecture material for CS 188 (Berkeley)

Attribution. Modified from Stuart Russell s slides (Berkeley) Parts of the slides are inspired by Dan Klein s lecture material for CS 188 (Berkeley) Machine Learning 1 Attribution Modified from Stuart Russell s slides (Berkeley) Parts of the slides are inspired by Dan Klein s lecture material for CS 188 (Berkeley) 2 Outline Inductive learning Decision

More information

Distributed forests for MapReduce-based machine learning

Distributed forests for MapReduce-based machine learning Distributed forests for MapReduce-based machine learning Ryoji Wakayama, Ryuei Murata, Akisato Kimura, Takayoshi Yamashita, Yuji Yamauchi, Hironobu Fujiyoshi Chubu University, Japan. NTT Communication

More information

Maximize Revenues on your Customer Loyalty Program using Predictive Analytics

Maximize Revenues on your Customer Loyalty Program using Predictive Analytics Maximize Revenues on your Customer Loyalty Program using Predictive Analytics 27 th Feb 14 Free Webinar by Before we begin... www Q & A? Your Speakers @parikh_shachi Technical Analyst @tatvic Loves js

More information

DATA MINING CLUSTER ANALYSIS: BASIC CONCEPTS

DATA MINING CLUSTER ANALYSIS: BASIC CONCEPTS DATA MINING CLUSTER ANALYSIS: BASIC CONCEPTS 1 AND ALGORITHMS Chiara Renso KDD-LAB ISTI- CNR, Pisa, Italy WHAT IS CLUSTER ANALYSIS? Finding groups of objects such that the objects in a group will be similar

More information

Big Data Analytics CSCI 4030

Big Data Analytics CSCI 4030 High dim. data Graph data Infinite data Machine learning Apps Locality sensitive hashing PageRank, SimRank Filtering data streams SVM Recommen der systems Clustering Community Detection Web advertising

More information

Tree based ensemble models regularization by convex optimization

Tree based ensemble models regularization by convex optimization Tree based ensemble models regularization by convex optimization Bertrand Cornélusse, Pierre Geurts and Louis Wehenkel Department of Electrical Engineering and Computer Science University of Liège B-4000

More information

Fast Analytics on Big Data with H20

Fast Analytics on Big Data with H20 Fast Analytics on Big Data with H20 0xdata.com, h2o.ai Tomas Nykodym, Petr Maj Team About H2O and 0xdata H2O is a platform for distributed in memory predictive analytics and machine learning Pure Java,

More information

RANDOM PROJECTIONS FOR SEARCH AND MACHINE LEARNING

RANDOM PROJECTIONS FOR SEARCH AND MACHINE LEARNING = + RANDOM PROJECTIONS FOR SEARCH AND MACHINE LEARNING Stefan Savev Berlin Buzzwords June 2015 KEYWORD-BASED SEARCH Document Data 300 unique words per document 300 000 words in vocabulary Data sparsity:

More information

Active Learning SVM for Blogs recommendation

Active Learning SVM for Blogs recommendation Active Learning SVM for Blogs recommendation Xin Guan Computer Science, George Mason University Ⅰ.Introduction In the DH Now website, they try to review a big amount of blogs and articles and find the

More information

Bilinear Prediction Using Low-Rank Models

Bilinear Prediction Using Low-Rank Models Bilinear Prediction Using Low-Rank Models Inderjit S. Dhillon Dept of Computer Science UT Austin 26th International Conference on Algorithmic Learning Theory Banff, Canada Oct 6, 2015 Joint work with C-J.

More information

Invited Applications Paper

Invited Applications Paper Invited Applications Paper - - Thore Graepel Joaquin Quiñonero Candela Thomas Borchert Ralf Herbrich Microsoft Research Ltd., 7 J J Thomson Avenue, Cambridge CB3 0FB, UK THOREG@MICROSOFT.COM JOAQUINC@MICROSOFT.COM

More information

Introduction to Machine Learning. Speaker: Harry Chao Advisor: J.J. Ding Date: 1/27/2011

Introduction to Machine Learning. Speaker: Harry Chao Advisor: J.J. Ding Date: 1/27/2011 Introduction to Machine Learning Speaker: Harry Chao Advisor: J.J. Ding Date: 1/27/2011 1 Outline 1. What is machine learning? 2. The basic of machine learning 3. Principles and effects of machine learning

More information

Can we Analyze all the Big Data we Collect?

Can we Analyze all the Big Data we Collect? DBKDA/WEB Panel 2015, Rome 28.05.2015 DBKDA Panel 2015, Rome, 27.05.2015 Reutlingen University Can we Analyze all the Big Data we Collect? Moderation: Fritz Laux, Reutlingen University, Germany Panelists:

More information

Decision Trees from large Databases: SLIQ

Decision Trees from large Databases: SLIQ Decision Trees from large Databases: SLIQ C4.5 often iterates over the training set How often? If the training set does not fit into main memory, swapping makes C4.5 unpractical! SLIQ: Sort the values

More information

BIG DATA What it is and how to use?

BIG DATA What it is and how to use? BIG DATA What it is and how to use? Lauri Ilison, PhD Data Scientist 21.11.2014 Big Data definition? There is no clear definition for BIG DATA BIG DATA is more of a concept than precise term 1 21.11.14

More information

Lecture 10: Regression Trees

Lecture 10: Regression Trees Lecture 10: Regression Trees 36-350: Data Mining October 11, 2006 Reading: Textbook, sections 5.2 and 10.5. The next three lectures are going to be about a particular kind of nonlinear predictive model,

More information

Streamdrill: Analyzing Big Data Streams in Realtime

Streamdrill: Analyzing Big Data Streams in Realtime Streamdrill: Analyzing Big Data Streams in Realtime Mikio L. Braun mikio@streamdrill.com @mikiobraun th 6 Realtime Big Data: Sources Finance Gaming Monitoring Advertisment Sensor Networks Social Media

More information

Large-Scale Data Sets Clustering Based on MapReduce and Hadoop

Large-Scale Data Sets Clustering Based on MapReduce and Hadoop Journal of Computational Information Systems 7: 16 (2011) 5956-5963 Available at http://www.jofcis.com Large-Scale Data Sets Clustering Based on MapReduce and Hadoop Ping ZHOU, Jingsheng LEI, Wenjun YE

More information

CSE 473: Artificial Intelligence Autumn 2010

CSE 473: Artificial Intelligence Autumn 2010 CSE 473: Artificial Intelligence Autumn 2010 Machine Learning: Naive Bayes and Perceptron Luke Zettlemoyer Many slides over the course adapted from Dan Klein. 1 Outline Learning: Naive Bayes and Perceptron

More information

15.564 Information Technology I. Business Intelligence

15.564 Information Technology I. Business Intelligence 15.564 Information Technology I Business Intelligence Outline Operational vs. Decision Support Systems What is Data Mining? Overview of Data Mining Techniques Overview of Data Mining Process Data Warehouses

More information

Sibyl: a system for large scale machine learning

Sibyl: a system for large scale machine learning Sibyl: a system for large scale machine learning Tushar Chandra, Eugene Ie, Kenneth Goldman, Tomas Lloret Llinares, Jim McFadden, Fernando Pereira, Joshua Redstone, Tal Shaked, Yoram Singer Machine Learning

More information

Data Mining Practical Machine Learning Tools and Techniques. Slides for Chapter 7 of Data Mining by I. H. Witten and E. Frank

Data Mining Practical Machine Learning Tools and Techniques. Slides for Chapter 7 of Data Mining by I. H. Witten and E. Frank Data Mining Practical Machine Learning Tools and Techniques Slides for Chapter 7 of Data Mining by I. H. Witten and E. Frank Engineering the input and output Attribute selection Scheme independent, scheme

More information

Predict the Popularity of YouTube Videos Using Early View Data

Predict the Popularity of YouTube Videos Using Early View Data 000 001 002 003 004 005 006 007 008 009 010 011 012 013 014 015 016 017 018 019 020 021 022 023 024 025 026 027 028 029 030 031 032 033 034 035 036 037 038 039 040 041 042 043 044 045 046 047 048 049 050

More information

Measuring the online experience of auto insurance companies

Measuring the online experience of auto insurance companies AUTO INSURANCE BENCHMARK STUDY Measuring the online experience of auto insurance companies Author: Ann Rochanayon Sr. Director of UX/CX Research at UserZoom Contents Introduction Study Methodology Summary

More information

Machine Learning over Big Data

Machine Learning over Big Data Machine Learning over Big Presented by Fuhao Zou fuhao@hust.edu.cn Jue 16, 2014 Huazhong University of Science and Technology Contents 1 2 3 4 Role of Machine learning Challenge of Big Analysis Distributed

More information

Predicting Flight Delays

Predicting Flight Delays Predicting Flight Delays Dieterich Lawson jdlawson@stanford.edu William Castillo will.castillo@stanford.edu Introduction Every year approximately 20% of airline flights are delayed or cancelled, costing

More information

IJCSES Vol.7 No.4 October 2013 pp.165-168 Serials Publications BEHAVIOR PERDITION VIA MINING SOCIAL DIMENSIONS

IJCSES Vol.7 No.4 October 2013 pp.165-168 Serials Publications BEHAVIOR PERDITION VIA MINING SOCIAL DIMENSIONS IJCSES Vol.7 No.4 October 2013 pp.165-168 Serials Publications BEHAVIOR PERDITION VIA MINING SOCIAL DIMENSIONS V.Sudhakar 1 and G. Draksha 2 Abstract:- Collective behavior refers to the behaviors of individuals

More information

Automated Model Based Testing for an Web Applications

Automated Model Based Testing for an Web Applications Automated Model Based Testing for an Web Applications Agasarpa Mounica, Lokanadham Naidu Vadlamudi Abstract- As the development of web applications plays a major role in our day-to-day life. Modeling the

More information

Statistical Machine Learning

Statistical Machine Learning Statistical Machine Learning UoC Stats 37700, Winter quarter Lecture 4: classical linear and quadratic discriminants. 1 / 25 Linear separation For two classes in R d : simple idea: separate the classes

More information

Chapter 6. The stacking ensemble approach

Chapter 6. The stacking ensemble approach 82 This chapter proposes the stacking ensemble approach for combining different data mining classifiers to get better performance. Other combination techniques like voting, bagging etc are also described

More information

The Scientific Data Mining Process

The Scientific Data Mining Process Chapter 4 The Scientific Data Mining Process When I use a word, Humpty Dumpty said, in rather a scornful tone, it means just what I choose it to mean neither more nor less. Lewis Carroll [87, p. 214] In

More information

Bayesian Machine Learning (ML): Modeling And Inference in Big Data. Zhuhua Cai Google, Rice University caizhua@gmail.com

Bayesian Machine Learning (ML): Modeling And Inference in Big Data. Zhuhua Cai Google, Rice University caizhua@gmail.com Bayesian Machine Learning (ML): Modeling And Inference in Big Data Zhuhua Cai Google Rice University caizhua@gmail.com 1 Syllabus Bayesian ML Concepts (Today) Bayesian ML on MapReduce (Next morning) Bayesian

More information

Introduction to Clustering

Introduction to Clustering 1/57 Introduction to Clustering Mark Johnson Department of Computing Macquarie University 2/57 Outline Supervised versus unsupervised learning Applications of clustering in text processing Evaluating clustering

More information

FUZZY CLUSTERING ANALYSIS OF DATA MINING: APPLICATION TO AN ACCIDENT MINING SYSTEM

FUZZY CLUSTERING ANALYSIS OF DATA MINING: APPLICATION TO AN ACCIDENT MINING SYSTEM International Journal of Innovative Computing, Information and Control ICIC International c 0 ISSN 34-48 Volume 8, Number 8, August 0 pp. 4 FUZZY CLUSTERING ANALYSIS OF DATA MINING: APPLICATION TO AN ACCIDENT

More information

Using multiple models: Bagging, Boosting, Ensembles, Forests

Using multiple models: Bagging, Boosting, Ensembles, Forests Using multiple models: Bagging, Boosting, Ensembles, Forests Bagging Combining predictions from multiple models Different models obtained from bootstrap samples of training data Average predictions or

More information

The Operational Value of Social Media Information. Social Media and Customer Interaction

The Operational Value of Social Media Information. Social Media and Customer Interaction The Operational Value of Social Media Information Dennis J. Zhang (Kellogg School of Management) Ruomeng Cui (Kelley School of Business) Santiago Gallino (Tuck School of Business) Antonio Moreno-Garcia

More information

Probabilistic Models for Big Data. Alex Davies and Roger Frigola University of Cambridge 13th February 2014

Probabilistic Models for Big Data. Alex Davies and Roger Frigola University of Cambridge 13th February 2014 Probabilistic Models for Big Data Alex Davies and Roger Frigola University of Cambridge 13th February 2014 The State of Big Data Why probabilistic models for Big Data? 1. If you don t have to worry about

More information

Extreme Computing. Big Data. Stratis Viglas. School of Informatics University of Edinburgh sviglas@inf.ed.ac.uk. Stratis Viglas Extreme Computing 1

Extreme Computing. Big Data. Stratis Viglas. School of Informatics University of Edinburgh sviglas@inf.ed.ac.uk. Stratis Viglas Extreme Computing 1 Extreme Computing Big Data Stratis Viglas School of Informatics University of Edinburgh sviglas@inf.ed.ac.uk Stratis Viglas Extreme Computing 1 Petabyte Age Big Data Challenges Stratis Viglas Extreme Computing

More information

Machine Learning. Mausam (based on slides by Tom Mitchell, Oren Etzioni and Pedro Domingos)

Machine Learning. Mausam (based on slides by Tom Mitchell, Oren Etzioni and Pedro Domingos) Machine Learning Mausam (based on slides by Tom Mitchell, Oren Etzioni and Pedro Domingos) What Is Machine Learning? A computer program is said to learn from experience E with respect to some class of

More information

Asking Hard Graph Questions. Paul Burkhardt. February 3, 2014

Asking Hard Graph Questions. Paul Burkhardt. February 3, 2014 Beyond Watson: Predictive Analytics and Big Data U.S. National Security Agency Research Directorate - R6 Technical Report February 3, 2014 300 years before Watson there was Euler! The first (Jeopardy!)

More information

Introduction to Machine Learning Lecture 1. Mehryar Mohri Courant Institute and Google Research mohri@cims.nyu.edu

Introduction to Machine Learning Lecture 1. Mehryar Mohri Courant Institute and Google Research mohri@cims.nyu.edu Introduction to Machine Learning Lecture 1 Mehryar Mohri Courant Institute and Google Research mohri@cims.nyu.edu Introduction Logistics Prerequisites: basics concepts needed in probability and statistics

More information

Part III: Machine Learning. CS 188: Artificial Intelligence. Machine Learning This Set of Slides. Parameter Estimation. Estimation: Smoothing

Part III: Machine Learning. CS 188: Artificial Intelligence. Machine Learning This Set of Slides. Parameter Estimation. Estimation: Smoothing CS 188: Artificial Intelligence Lecture 20: Dynamic Bayes Nets, Naïve Bayes Pieter Abbeel UC Berkeley Slides adapted from Dan Klein. Part III: Machine Learning Up until now: how to reason in a model and

More information

An Introduction to Data Mining. Big Data World. Related Fields and Disciplines. What is Data Mining? 2/12/2015

An Introduction to Data Mining. Big Data World. Related Fields and Disciplines. What is Data Mining? 2/12/2015 An Introduction to Data Mining for Wind Power Management Spring 2015 Big Data World Every minute: Google receives over 4 million search queries Facebook users share almost 2.5 million pieces of content

More information

Applied Data Mining Analysis: A Step-by-Step Introduction Using Real-World Data Sets

Applied Data Mining Analysis: A Step-by-Step Introduction Using Real-World Data Sets Applied Data Mining Analysis: A Step-by-Step Introduction Using Real-World Data Sets http://info.salford-systems.com/jsm-2015-ctw August 2015 Salford Systems Course Outline Demonstration of two classification

More information

E6895 Advanced Big Data Analytics Lecture 3:! Spark and Data Analytics

E6895 Advanced Big Data Analytics Lecture 3:! Spark and Data Analytics E6895 Advanced Big Data Analytics Lecture 3:! Spark and Data Analytics Ching-Yung Lin, Ph.D. Adjunct Professor, Dept. of Electrical Engineering and Computer Science Mgr., Dept. of Network Science and Big

More information

Search Taxonomy. Web Search. Search Engine Optimization. Information Retrieval

Search Taxonomy. Web Search. Search Engine Optimization. Information Retrieval Information Retrieval INFO 4300 / CS 4300! Retrieval models Older models» Boolean retrieval» Vector Space model Probabilistic Models» BM25» Language models Web search» Learning to Rank Search Taxonomy!

More information

Client Based Power Iteration Clustering Algorithm to Reduce Dimensionality in Big Data

Client Based Power Iteration Clustering Algorithm to Reduce Dimensionality in Big Data Client Based Power Iteration Clustering Algorithm to Reduce Dimensionalit in Big Data Jaalatchum. D 1, Thambidurai. P 1, Department of CSE, PKIET, Karaikal, India Abstract - Clustering is a group of objects

More information

Bringing Big Data Modelling into the Hands of Domain Experts

Bringing Big Data Modelling into the Hands of Domain Experts Bringing Big Data Modelling into the Hands of Domain Experts David Willingham Senior Application Engineer MathWorks david.willingham@mathworks.com.au 2015 The MathWorks, Inc. 1 Data is the sword of the

More information

Semi-Supervised Support Vector Machines and Application to Spam Filtering

Semi-Supervised Support Vector Machines and Application to Spam Filtering Semi-Supervised Support Vector Machines and Application to Spam Filtering Alexander Zien Empirical Inference Department, Bernhard Schölkopf Max Planck Institute for Biological Cybernetics ECML 2006 Discovery

More information

Social Search. Communities of users actively participating in the search process

Social Search. Communities of users actively participating in the search process Chapter 1 Social Search Social Search Social search Communities of users actively participating in the search process Goes beyond classical search tasks Key differences Users interact with the system Users

More information

Data Mining and Predictive Analytics - Assignment 1 Image Popularity Prediction on Social Networks

Data Mining and Predictive Analytics - Assignment 1 Image Popularity Prediction on Social Networks Data Mining and Predictive Analytics - Assignment 1 Image Popularity Prediction on Social Networks Wei-Tang Liao and Jong-Chyi Su Department of Computer Science and Engineering University of California,

More information

Classification of Bad Accounts in Credit Card Industry

Classification of Bad Accounts in Credit Card Industry Classification of Bad Accounts in Credit Card Industry Chengwei Yuan December 12, 2014 Introduction Risk management is critical for a credit card company to survive in such competing industry. In addition

More information

Question 2 Naïve Bayes (16 points)

Question 2 Naïve Bayes (16 points) Question 2 Naïve Bayes (16 points) About 2/3 of your email is spam so you downloaded an open source spam filter based on word occurrences that uses the Naive Bayes classifier. Assume you collected the

More information

Bootstrapping Big Data

Bootstrapping Big Data Bootstrapping Big Data Ariel Kleiner Ameet Talwalkar Purnamrita Sarkar Michael I. Jordan Computer Science Division University of California, Berkeley {akleiner, ameet, psarkar, jordan}@eecs.berkeley.edu

More information

Finding Advertising Keywords on Web Pages. Contextual Ads 101

Finding Advertising Keywords on Web Pages. Contextual Ads 101 Finding Advertising Keywords on Web Pages Scott Wen-tau Yih Joshua Goodman Microsoft Research Vitor R. Carvalho Carnegie Mellon University Contextual Ads 101 Publisher s website Digital Camera Review The

More information

Linear Threshold Units

Linear Threshold Units Linear Threshold Units w x hx (... w n x n w We assume that each feature x j and each weight w j is a real number (we will relax this later) We will study three different algorithms for learning linear

More information

Comparison of Non-linear Dimensionality Reduction Techniques for Classification with Gene Expression Microarray Data

Comparison of Non-linear Dimensionality Reduction Techniques for Classification with Gene Expression Microarray Data CMPE 59H Comparison of Non-linear Dimensionality Reduction Techniques for Classification with Gene Expression Microarray Data Term Project Report Fatma Güney, Kübra Kalkan 1/15/2013 Keywords: Non-linear

More information

CI6227: Data Mining. Lesson 11b: Ensemble Learning. Data Analytics Department, Institute for Infocomm Research, A*STAR, Singapore.

CI6227: Data Mining. Lesson 11b: Ensemble Learning. Data Analytics Department, Institute for Infocomm Research, A*STAR, Singapore. CI6227: Data Mining Lesson 11b: Ensemble Learning Sinno Jialin PAN Data Analytics Department, Institute for Infocomm Research, A*STAR, Singapore Acknowledgements: slides are adapted from the lecture notes

More information

Artificial Neural Networks and Support Vector Machines. CS 486/686: Introduction to Artificial Intelligence

Artificial Neural Networks and Support Vector Machines. CS 486/686: Introduction to Artificial Intelligence Artificial Neural Networks and Support Vector Machines CS 486/686: Introduction to Artificial Intelligence 1 Outline What is a Neural Network? - Perceptron learners - Multi-layer networks What is a Support

More information

Introduction to Support Vector Machines. Colin Campbell, Bristol University

Introduction to Support Vector Machines. Colin Campbell, Bristol University Introduction to Support Vector Machines Colin Campbell, Bristol University 1 Outline of talk. Part 1. An Introduction to SVMs 1.1. SVMs for binary classification. 1.2. Soft margins and multi-class classification.

More information

Neural Network Add-in

Neural Network Add-in Neural Network Add-in Version 1.5 Software User s Guide Contents Overview... 2 Getting Started... 2 Working with Datasets... 2 Open a Dataset... 3 Save a Dataset... 3 Data Pre-processing... 3 Lagging...

More information

W6.B.1. FAQs CS535 BIG DATA W6.B.3. 4. If the distance of the point is additionally less than the tight distance T 2, remove it from the original set

W6.B.1. FAQs CS535 BIG DATA W6.B.3. 4. If the distance of the point is additionally less than the tight distance T 2, remove it from the original set http://wwwcscolostateedu/~cs535 W6B W6B2 CS535 BIG DAA FAQs Please prepare for the last minute rush Store your output files safely Partial score will be given for the output from less than 50GB input Computer

More information

Clustering. Danilo Croce Web Mining & Retrieval a.a. 2015/201 16/03/2016

Clustering. Danilo Croce Web Mining & Retrieval a.a. 2015/201 16/03/2016 Clustering Danilo Croce Web Mining & Retrieval a.a. 2015/201 16/03/2016 1 Supervised learning vs. unsupervised learning Supervised learning: discover patterns in the data that relate data attributes with

More information

Steven C.H. Hoi. School of Computer Engineering Nanyang Technological University Singapore

Steven C.H. Hoi. School of Computer Engineering Nanyang Technological University Singapore Steven C.H. Hoi School of Computer Engineering Nanyang Technological University Singapore Acknowledgments: Peilin Zhao, Jialei Wang, Hao Xia, Jing Lu, Rong Jin, Pengcheng Wu, Dayong Wang, etc. 2 Agenda

More information

HT2015: SC4 Statistical Data Mining and Machine Learning

HT2015: SC4 Statistical Data Mining and Machine Learning HT2015: SC4 Statistical Data Mining and Machine Learning Dino Sejdinovic Department of Statistics Oxford http://www.stats.ox.ac.uk/~sejdinov/sdmml.html Bayesian Nonparametrics Parametric vs Nonparametric

More information

Supervised Learning Evaluation (via Sentiment Analysis)!

Supervised Learning Evaluation (via Sentiment Analysis)! Supervised Learning Evaluation (via Sentiment Analysis)! Why Analyze Sentiment? Sentiment Analysis (Opinion Mining) Automatically label documents with their sentiment Toward a topic Aggregated over documents

More information

Crowdclustering with Sparse Pairwise Labels: A Matrix Completion Approach

Crowdclustering with Sparse Pairwise Labels: A Matrix Completion Approach Outline Crowdclustering with Sparse Pairwise Labels: A Matrix Completion Approach Jinfeng Yi, Rong Jin, Anil K. Jain, Shaili Jain 2012 Presented By : KHALID ALKOBAYER Crowdsourcing and Crowdclustering

More information

Dynamics of Genre and Domain Intents

Dynamics of Genre and Domain Intents Dynamics of Genre and Domain Intents Shanu Sushmita, Benjamin Piwowarski, and Mounia Lalmas University of Glasgow {shanu,bpiwowar,mounia}@dcs.gla.ac.uk Abstract. As the type of content available on the

More information

Machine Learning. CUNY Graduate Center, Spring 2013. Professor Liang Huang. huang@cs.qc.cuny.edu

Machine Learning. CUNY Graduate Center, Spring 2013. Professor Liang Huang. huang@cs.qc.cuny.edu Machine Learning CUNY Graduate Center, Spring 2013 Professor Liang Huang huang@cs.qc.cuny.edu http://acl.cs.qc.edu/~lhuang/teaching/machine-learning Logistics Lectures M 9:30-11:30 am Room 4419 Personnel

More information

Fast Matching of Binary Features

Fast Matching of Binary Features Fast Matching of Binary Features Marius Muja and David G. Lowe Laboratory for Computational Intelligence University of British Columbia, Vancouver, Canada {mariusm,lowe}@cs.ubc.ca Abstract There has been

More information

Outlier Ensembles. Charu C. Aggarwal IBM T J Watson Research Center Yorktown, NY 10598. Keynote, Outlier Detection and Description Workshop, 2013

Outlier Ensembles. Charu C. Aggarwal IBM T J Watson Research Center Yorktown, NY 10598. Keynote, Outlier Detection and Description Workshop, 2013 Charu C. Aggarwal IBM T J Watson Research Center Yorktown, NY 10598 Outlier Ensembles Keynote, Outlier Detection and Description Workshop, 2013 Based on the ACM SIGKDD Explorations Position Paper: Outlier

More information

STA 4273H: Statistical Machine Learning

STA 4273H: Statistical Machine Learning STA 4273H: Statistical Machine Learning Russ Salakhutdinov Department of Statistics! rsalakhu@utstat.toronto.edu! http://www.cs.toronto.edu/~rsalakhu/ Lecture 6 Three Approaches to Classification Construct

More information

CS Master Level Courses and Areas COURSE DESCRIPTIONS. CSCI 521 Real-Time Systems. CSCI 522 High Performance Computing

CS Master Level Courses and Areas COURSE DESCRIPTIONS. CSCI 521 Real-Time Systems. CSCI 522 High Performance Computing CS Master Level Courses and Areas The graduate courses offered may change over time, in response to new developments in computer science and the interests of faculty and students; the list of graduate

More information

Data Mining. Nonlinear Classification

Data Mining. Nonlinear Classification Data Mining Unit # 6 Sajjad Haider Fall 2014 1 Nonlinear Classification Classes may not be separable by a linear boundary Suppose we randomly generate a data set as follows: X has range between 0 to 15

More information

Multi-Class and Structured Classification

Multi-Class and Structured Classification Multi-Class and Structured Classification [slides prises du cours cs294-10 UC Berkeley (2006 / 2009)] [ p y( )] http://www.cs.berkeley.edu/~jordan/courses/294-fall09 Basic Classification in ML Input Output

More information

A Simple Feature Extraction Technique of a Pattern By Hopfield Network

A Simple Feature Extraction Technique of a Pattern By Hopfield Network A Simple Feature Extraction Technique of a Pattern By Hopfield Network A.Nag!, S. Biswas *, D. Sarkar *, P.P. Sarkar *, B. Gupta **! Academy of Technology, Hoogly - 722 *USIC, University of Kalyani, Kalyani

More information

Leveraging Ensemble Models in SAS Enterprise Miner

Leveraging Ensemble Models in SAS Enterprise Miner ABSTRACT Paper SAS133-2014 Leveraging Ensemble Models in SAS Enterprise Miner Miguel Maldonado, Jared Dean, Wendy Czika, and Susan Haller SAS Institute Inc. Ensemble models combine two or more models to

More information

Class #6: Non-linear classification. ML4Bio 2012 February 17 th, 2012 Quaid Morris

Class #6: Non-linear classification. ML4Bio 2012 February 17 th, 2012 Quaid Morris Class #6: Non-linear classification ML4Bio 2012 February 17 th, 2012 Quaid Morris 1 Module #: Title of Module 2 Review Overview Linear separability Non-linear classification Linear Support Vector Machines

More information

E-commerce Transaction Anomaly Classification

E-commerce Transaction Anomaly Classification E-commerce Transaction Anomaly Classification Minyong Lee minyong@stanford.edu Seunghee Ham sham12@stanford.edu Qiyi Jiang qjiang@stanford.edu I. INTRODUCTION Due to the increasing popularity of e-commerce

More information

Simple and efficient online algorithms for real world applications

Simple and efficient online algorithms for real world applications Simple and efficient online algorithms for real world applications Università degli Studi di Milano Milano, Italy Talk @ Centro de Visión por Computador Something about me PhD in Robotics at LIRA-Lab,

More information

Homework 2. Page 154: Exercise 8.10. Page 145: Exercise 8.3 Page 150: Exercise 8.9

Homework 2. Page 154: Exercise 8.10. Page 145: Exercise 8.3 Page 150: Exercise 8.9 Homework 2 Page 110: Exercise 6.10; Exercise 6.12 Page 116: Exercise 6.15; Exercise 6.17 Page 121: Exercise 6.19 Page 122: Exercise 6.20; Exercise 6.23; Exercise 6.24 Page 131: Exercise 7.3; Exercise 7.5;

More information

Content-Based Recommendation

Content-Based Recommendation Content-Based Recommendation Content-based? Item descriptions to identify items that are of particular interest to the user Example Example Comparing with Noncontent based Items User-based CF Searches

More information

Chapter ML:XI (continued)

Chapter ML:XI (continued) Chapter ML:XI (continued) XI. Cluster Analysis Data Mining Overview Cluster Analysis Basics Hierarchical Cluster Analysis Iterative Cluster Analysis Density-Based Cluster Analysis Cluster Evaluation Constrained

More information

Distance Metric Learning in Data Mining (Part I) Fei Wang and Jimeng Sun IBM TJ Watson Research Center

Distance Metric Learning in Data Mining (Part I) Fei Wang and Jimeng Sun IBM TJ Watson Research Center Distance Metric Learning in Data Mining (Part I) Fei Wang and Jimeng Sun IBM TJ Watson Research Center 1 Outline Part I - Applications Motivation and Introduction Patient similarity application Part II

More information

Neural Networks and Support Vector Machines

Neural Networks and Support Vector Machines INF5390 - Kunstig intelligens Neural Networks and Support Vector Machines Roar Fjellheim INF5390-13 Neural Networks and SVM 1 Outline Neural networks Perceptrons Neural networks Support vector machines

More information

Tackling Big Data with R

Tackling Big Data with R New features and old concepts for handling large and streaming data in practice Simon Urbanek R Foundation Overview Motivation Custom connections Data processing pipelines Parallel processing Back-end

More information