Steven C.H. Hoi. School of Computer Engineering Nanyang Technological University Singapore
|
|
- Roy Stanley
- 8 years ago
- Views:
Transcription
1 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 2
3 Agenda Introduction Big Data: Opportunities & Challenges Online Learning: What & Why Online Learning for Living Data Analytics Research Challenges Online Feature Selection Online Collaborative Filtering Online Multiple Kernel Learning Conclusions 3
4 Big Data: Popularity Google Trends Big Hope or Big Hype 4
5 What is Big Data Volume Velocity Variety Source: 5
6 Big Data: Big Value Source from McKinsey 6
7 Big Data: Opportunities 7
8 Big Data: Challenges Volume Efficiency Handle vast volume of data (million or even billion) with limited computing capacity (CPU/RAM/DISK) Scalability Be able to scale up to handle exploding data (e.g., real-time data streams) Velocity Big Online Data Learning Analytics Variety Adaptability Be able to adapt complex and changing environment to deal with diverse data 8
9 What is Online Learning? Batch/Offline Learning Online Learning Feedback Learner Update Predictor 9
10 Example: Perceptron algorithm (Rosenblatt 1958) w 1 w 3 + w 2-10
11 Why Online Learning? Avoid re-training when adding new data High efficiency Excellent scalability Strong adaptability to changing environments Simple to understand Trivial to implement Easy to be parallelized Theoretical guarantee 11
12 Agenda Introduction Big Data: Opportunities & Challenges Online Learning: What & Why Online Learning for Living Data Analytics Research Challenges Online Feature Selection Online Collaborative Filtering Online Multiple Kernel Learning Conclusions 12
13 Challenges of Living Data Analytics High Dimensionality High Sparsity High Variety 13
14 Online Learning Methods Online Feature Selection (BigMine13, TKDE14) To select a subset of informative features in machine learning tasks for analyzing data with high dimensionality Online Collaborative Filtering (ACML 13, RecSys 13,) To learn from a sequence of rating data (sparse rating matrix) for resolving recommender systems Online Multiple Kernel Learning (ML 13, TPAMI 14) To fuse multiple types of diverse data sources by multiple kernels based machine learning where each kernel represents each type of data/representation 14
15 Online Feature Selection Feature Selection Select a subset of informative features and remove irrelevant/redundant features for model construction Alleviate the curse of dimensionality, speed up the learning task, improve the interpretability From offline/batch to Online learning Online learner is allowed to maintain a classifier by involving only a small fixed number of features The challenge is how to make accurate prediction on an instance using a small number of active features. 15
16 Online Feature Selection Family of Online Feature Selection algorithms Key idea: exploring sparse online learning Online Feature Selection (Hoi et al. BigMine13, TKDE14) Online Gradient Descent or Perceptron Sparse projection and truncation 16
17 Empirical Evaluation Batch Algorithms LIBLINEAR (Fan et al., JMLR'08): state-of-the-art fast linear classification tool FGM (Tan & Tsang ICML 10): state-of-the-art fast feature selection algorithm Online Algorithms Perceptron with truncation OFS: online FS via sparse projection Evaluation Metrics Classification accuracy Training time cost 17
18 Empirical Evaluation 18
19 Empirical Evaluation mrmr: mutual information criteria of maxdependency, max-relevance, and minredundancy (Peng et al TPAMI 05, cited over 2000+) 19
20 Scalability on Ultra-high Dimensional Data D - # dimensions N - # instances F - # non-zero features S - binary file size D = 10K N = 100K F = 30 Million S = 800MB D = 1 Billion N = 1 Million F = 1 Billion S = 13 GB (estimated) 20
21 Online Collaborative Filtering Collaborative Filtering (CF) It uses known preferences of other users to make prediction to the unknown preferences of other users. Challenges of living analytics Extremely sparse data Data arrive sequentially Batch CF algorithms have some critical limitations (e.g., high retraining cost) 21
22 Online Collaborative Filtering CF: from batch to online learning The learning process works sequentially for dealing with new rating data instances on the fly Make the recommender system evolve over time Existing approach for OCF Matrix factorization for dealing with sparse data First order algorithms, e.g., online gradient descent But suffer from slow convergence rate We propose second order online CF method Confidence weighted online collaborative filtering (CWOCF) algorithms (Jing, Wang, Hoi, ACML 13) Online Multi-Task Collaborative Filtering algorithms (Wang et al RecSys 13) 22
23 CWOCF: formulation Main objective function: Learn U and V from partly observed ratings R: Loss functions: 23
24 CWOCF: online update Assuming: Online learning w.r.t. each received rating: Online Update (w.r.t. RMSE): 24
25 Empirical Evaluation Compared algorithms: Datesets: 25
26 Empirical Evaluation 26
27 Online Multiple Kernel Learning Motivation Variety is a key challenge for living/big data analytics Traditional methods assume data in vector space Real objects often have diverse representations Multiple Kernel Representation Each kernel represents one similarity function Pyramid matching kernels (vision, multimedia) Graph kernels (bio, web/social, etc) Sequence kernels (speech, video, bio, etc) Tree kernels (NLP, etc) 27
28 Multiple Kernel Learning (MKL) What is Multiple Kernel Learning (MKL) (Lanckriet et al JMLRl04) Kernel method by an optimal combination of multiple kernels Batch MKL Formulation Hard to solve the convex-concave optimization for big data! Can we avoid solving the batch optimization directly? 28
29 Online MKL (Hoi et al., ML 13) Objective Aims to learn a kernel-based predictor with multiple kernels from a sequence of (multi-modal) data examples Avoid the need of solving complicated optimizations Main idea: a two-step online learning At each iteration, if there is a mistake: Step 1: Online learning with each single kernel Kernel Perceptron (Rosenblatt Frank, 1958, Freund 1999) Step 2: Online update the combination weights Hedge algorithm (Freund and Schapire COLT95) 29
30 Online MKL for Classification Comparisons Perceptron(u) Perceptron(* best) OM-2(MKL variant) OMKC 30
31 Online MKL for Multimedia Retrieval Online Multi-Kernel Similarity Learning (Xia et al TPAMI 14) Aim to learn multi-kernel similarity for multimedia retrieval Color Side Info Stream Texture OMKS Contentbased Multimedia Retrieval Local pattern (BoW) 31
32 Multi-modal Image Retrieval Query OASIS(*) OKS(*) OMKS-U OMKS OASIS(*) OKS(*) OMKS-U OMKS 32
33 Conclusion Introduction of emerging opportunities and challenges when machine learning meets big data Introduction of online learning, a promising family of machine learning techniques for living analytics with big data Present three online learning techniques to address different real-world challenges of living data analytics tasks 33
34 Take-Home Message Online learning is promising for living/big data analytics More challenges and opportunities ahead: More effective online learning algorithms Handle more real-world challenges, e.g., sparsity, highdimensionality, concept drifting, noise, etc. Scale up for mining billions of instances using distributed computing (e.g., Hadoop) & parallel programming (e.g., GPU) LIBOL: An open-source Library of Online Learning Algorithms Also available at JMLR MLOSS 34
Steven C.H. Hoi School of Information Systems Singapore Management University Email: chhoi@smu.edu.sg
Steven C.H. Hoi School of Information Systems Singapore Management University Email: chhoi@smu.edu.sg Introduction http://stevenhoi.org/ Finance Recommender Systems Cyber Security Machine Learning Visual
More informationSteven C.H. Hoi. Methods and Applications. School of Computer Engineering Nanyang Technological University Singapore 4 May, 2013
Methods and Applications Steven C.H. Hoi School of Computer Engineering Nanyang Technological University Singapore 4 May, 2013 http://libol.stevenhoi.org/ Acknowledgements Peilin Zhao Jialei Wang Rong
More informationOnline Learning Methods for Big Data Analytics
Online Learning Methods for Big Data Analytics Steven C.H. Hoi *, Peilin Zhao + * Singapore Management University + Institute for Infocomm Research, A*STAR 17 Dec 2014 http://libol.stevenhoi.org/ Agenda
More informationOnline Feature Selection for Mining Big Data
Online Feature Selection for Mining Big Data Steven C.H. Hoi, Jialei Wang, Peilin Zhao, Rong Jin School of Computer Engineering, Nanyang Technological University, Singapore Department of Computer Science
More informationDUOL: A Double Updating Approach for Online Learning
: A Double Updating Approach for Online Learning Peilin Zhao School of Comp. Eng. Nanyang Tech. University Singapore 69798 zhao6@ntu.edu.sg Steven C.H. Hoi School of Comp. Eng. Nanyang Tech. University
More informationSimple 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 informationParallel Data Mining. Team 2 Flash Coders Team Research Investigation Presentation 2. Foundations of Parallel Computing Oct 2014
Parallel Data Mining Team 2 Flash Coders Team Research Investigation Presentation 2 Foundations of Parallel Computing Oct 2014 Agenda Overview of topic Analysis of research papers Software design Overview
More informationChapter 4: Artificial Neural Networks
Chapter 4: Artificial Neural Networks CS 536: Machine Learning Littman (Wu, TA) Administration icml-03: instructional Conference on Machine Learning http://www.cs.rutgers.edu/~mlittman/courses/ml03/icml03/
More informationClassifying Large Data Sets Using SVMs with Hierarchical Clusters. Presented by :Limou Wang
Classifying Large Data Sets Using SVMs with Hierarchical Clusters Presented by :Limou Wang Overview SVM Overview Motivation Hierarchical micro-clustering algorithm Clustering-Based SVM (CB-SVM) Experimental
More informationMachine 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 informationArtificial 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 informationJournée 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 informationTowards a Thriving Data Economy: Open Data, Big Data, and Data Ecosystems
Towards a Thriving Data Economy: Open Data, Big Data, and Data Ecosystems Volker Markl volker.markl@tu-berlin.de dima.tu-berlin.de dfki.de/web/research/iam/ bbdc.berlin Based on my 2014 Vision Paper On
More informationSimilarity 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 informationAzure Machine Learning, SQL Data Mining and R
Azure Machine Learning, SQL Data Mining and R Day-by-day Agenda Prerequisites No formal prerequisites. Basic knowledge of SQL Server Data Tools, Excel and any analytical experience helps. Best of all:
More informationIntroduction to Online Learning Theory
Introduction to Online Learning Theory Wojciech Kot lowski Institute of Computing Science, Poznań University of Technology IDSS, 04.06.2013 1 / 53 Outline 1 Example: Online (Stochastic) Gradient Descent
More informationHUAWEI Advanced Data Science with Spark Streaming. Albert Bifet (@abifet)
HUAWEI Advanced Data Science with Spark Streaming Albert Bifet (@abifet) Huawei Noah s Ark Lab Focus Intelligent Mobile Devices Data Mining & Artificial Intelligence Intelligent Telecommunication Networks
More informationThe 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 informationScalable Machine Learning - or what to do with all that Big Data infrastructure
- or what to do with all that Big Data infrastructure TU Berlin blog.mikiobraun.de Strata+Hadoop World London, 2015 1 Complex Data Analysis at Scale Click-through prediction Personalized Spam Detection
More informationParallel & Distributed Optimization. Based on Mark Schmidt s slides
Parallel & Distributed Optimization Based on Mark Schmidt s slides Motivation behind using parallel & Distributed optimization Performance Computational throughput have increased exponentially in linear
More informationHow To Train A Classifier With Active Learning In Spam Filtering
Online Active Learning Methods for Fast Label-Efficient Spam Filtering D. Sculley Department of Computer Science Tufts University, Medford, MA USA dsculley@cs.tufts.edu ABSTRACT Active learning methods
More informationPractical Data Science with Azure Machine Learning, SQL Data Mining, and R
Practical Data Science with Azure Machine Learning, SQL Data Mining, and R Overview This 4-day class is the first of the two data science courses taught by Rafal Lukawiecki. Some of the topics will be
More informationBIG DATA & ANALYTICS. Transforming the business and driving revenue through big data and analytics
BIG DATA & ANALYTICS Transforming the business and driving revenue through big data and analytics Collection, storage and extraction of business value from data generated from a variety of sources are
More informationSupervised Learning (Big Data Analytics)
Supervised Learning (Big Data Analytics) Vibhav Gogate Department of Computer Science The University of Texas at Dallas Practical advice Goal of Big Data Analytics Uncover patterns in Data. Can be used
More informationScalable Developments for Big Data Analytics in Remote Sensing
Scalable Developments for Big Data Analytics in Remote Sensing Federated Systems and Data Division Research Group High Productivity Data Processing Dr.-Ing. Morris Riedel et al. Research Group Leader,
More informationA Simple Introduction to Support Vector Machines
A Simple Introduction to Support Vector Machines Martin Law Lecture for CSE 802 Department of Computer Science and Engineering Michigan State University Outline A brief history of SVM Large-margin linear
More informationTOWARD BIG DATA ANALYSIS WORKSHOP
TOWARD BIG DATA ANALYSIS WORKSHOP 邁 向 巨 量 資 料 分 析 研 討 會 摘 要 集 2015.06.05-06 巨 量 資 料 之 矩 陣 視 覺 化 陳 君 厚 中 央 研 究 院 統 計 科 學 研 究 所 摘 要 視 覺 化 (Visualization) 與 探 索 式 資 料 分 析 (Exploratory Data Analysis, EDA)
More informationParallel Data Selection Based on Neurodynamic Optimization in the Era of Big Data
Parallel Data Selection Based on Neurodynamic Optimization in the Era of Big Data Jun Wang Department of Mechanical and Automation Engineering The Chinese University of Hong Kong Shatin, New Territories,
More informationMachine Learning using MapReduce
Machine Learning using MapReduce What is Machine Learning Machine learning is a subfield of artificial intelligence concerned with techniques that allow computers to improve their outputs based on previous
More informationThe 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 informationData-stream Mining for Rule-based Access Control. Andrii Shalaginov, andrii.shalaginov@hig.no 13 th of October 2014 COINS PhD seminar
Data-stream Mining for Rule-based Access Control Andrii Shalaginov, andrii.shalaginov@hig.no 13 th of October 2014 COINS PhD seminar 2 / 182 OVERALL PICTURE OF PHD Work towards Data-driven Reasoning for
More informationUnsupervised Data Mining (Clustering)
Unsupervised Data Mining (Clustering) Javier Béjar KEMLG December 01 Javier Béjar (KEMLG) Unsupervised Data Mining (Clustering) December 01 1 / 51 Introduction Clustering in KDD One of the main tasks in
More informationThe Need for Training in Big Data: Experiences and Case Studies
The Need for Training in Big Data: Experiences and Case Studies Guy Lebanon Amazon Background and Disclaimer All opinions are mine; other perspectives are legitimate. Based on my experience as a professor
More informationA Potential-based Framework for Online Multi-class Learning with Partial Feedback
A Potential-based Framework for Online Multi-class Learning with Partial Feedback Shijun Wang Rong Jin Hamed Valizadegan Radiology and Imaging Sciences Computer Science and Engineering Computer Science
More informationBig Data Analytics. The Hype and the Hope* Dr. Ted Ralphs Industrial and Systems Engineering Director, COR@L Laboratory
Big Data Analytics The Hype and the Hope* Dr. Ted Ralphs Industrial and Systems Engineering Director, COR@L Laboratory * Source: http://www.economistinsights.com/technology-innovation/analysis/hype-and-hope/methodology
More informationInformation Management course
Università degli Studi di Milano Master Degree in Computer Science Information Management course Teacher: Alberto Ceselli Lecture 01 : 06/10/2015 Practical informations: Teacher: Alberto Ceselli (alberto.ceselli@unimi.it)
More informationActive 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 informationClustering Big Data. Anil K. Jain. (with Radha Chitta and Rong Jin) Department of Computer Science Michigan State University November 29, 2012
Clustering Big Data Anil K. Jain (with Radha Chitta and Rong Jin) Department of Computer Science Michigan State University November 29, 2012 Outline Big Data How to extract information? Data clustering
More informationData, Measurements, Features
Data, Measurements, Features Middle East Technical University Dep. of Computer Engineering 2009 compiled by V. Atalay What do you think of when someone says Data? We might abstract the idea that data are
More informationII. RELATED WORK. Sentiment Mining
Sentiment Mining Using Ensemble Classification Models Matthew Whitehead and Larry Yaeger Indiana University School of Informatics 901 E. 10th St. Bloomington, IN 47408 {mewhiteh, larryy}@indiana.edu Abstract
More informationLearning to Process Natural Language in Big Data Environment
CCF ADL 2015 Nanchang Oct 11, 2015 Learning to Process Natural Language in Big Data Environment Hang Li Noah s Ark Lab Huawei Technologies Part 1: Deep Learning - Present and Future Talk Outline Overview
More informationSanjeev Kumar. contribute
RESEARCH ISSUES IN DATAA MINING Sanjeev Kumar I.A.S.R.I., Library Avenue, Pusa, New Delhi-110012 sanjeevk@iasri.res.in 1. Introduction The field of data mining and knowledgee discovery is emerging as a
More informationTowards Scalable and Accurate Online Feature Selection for Big Data
2014 IEEE International Conference on Data Mining Towards Scalable and Accurate Online Feature Selection for Big Data Kui Yu 1, Xindong Wu 2, 3, Wei Ding 4, and Jian Pei 1 1 School of Computing Science,
More informationCar Insurance. Havránek, Pokorný, Tomášek
Car Insurance Havránek, Pokorný, Tomášek Outline Data overview Horizontal approach + Decision tree/forests Vertical (column) approach + Neural networks SVM Data overview Customers Viewed policies Bought
More informationMultiple Kernel Learning on the Limit Order Book
JMLR: Workshop and Conference Proceedings 11 (2010) 167 174 Workshop on Applications of Pattern Analysis Multiple Kernel Learning on the Limit Order Book Tristan Fletcher Zakria Hussain John Shawe-Taylor
More informationSupervised Feature Selection & Unsupervised Dimensionality Reduction
Supervised Feature Selection & Unsupervised Dimensionality Reduction Feature Subset Selection Supervised: class labels are given Select a subset of the problem features Why? Redundant features much or
More informationPATTERN RECOGNITION AND MACHINE LEARNING CHAPTER 4: LINEAR MODELS FOR CLASSIFICATION
PATTERN RECOGNITION AND MACHINE LEARNING CHAPTER 4: LINEAR MODELS FOR CLASSIFICATION Introduction In the previous chapter, we explored a class of regression models having particularly simple analytical
More informationSense Making in an IOT World: Sensor Data Analysis with Deep Learning
Sense Making in an IOT World: Sensor Data Analysis with Deep Learning Natalia Vassilieva, PhD Senior Research Manager GTC 2016 Deep learning proof points as of today Vision Speech Text Other Search & information
More informationCost-Sensitive Online Active Learning with Application to Malicious URL Detection
Cost-Sensitive Online Active Learning with Application to Malicious URL Detection ABSTRACT Peilin Zhao School of Computer Engineering Nanyang Technological University 50 Nanyang Avenue, Singapore 639798
More informationCOMP 598 Applied Machine Learning Lecture 21: Parallelization methods for large-scale machine learning! Big Data by the numbers
COMP 598 Applied Machine Learning Lecture 21: Parallelization methods for large-scale machine learning! Instructor: (jpineau@cs.mcgill.ca) TAs: Pierre-Luc Bacon (pbacon@cs.mcgill.ca) Ryan Lowe (ryan.lowe@mail.mcgill.ca)
More informationDecision 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 informationLearning is a very general term denoting the way in which agents:
What is learning? Learning is a very general term denoting the way in which agents: Acquire and organize knowledge (by building, modifying and organizing internal representations of some external reality);
More informationBringing 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 informationL25: Ensemble learning
L25: Ensemble learning Introduction Methods for constructing ensembles Combination strategies Stacked generalization Mixtures of experts Bagging Boosting CSCE 666 Pattern Analysis Ricardo Gutierrez-Osuna
More informationTopics in basic DBMS course
Topics in basic DBMS course Database design Transaction processing Relational query languages (SQL), calculus, and algebra DBMS APIs Database tuning (physical database design) Basic query processing (ch
More informationMachine 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 informationAn 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 informationD-optimal plans in observational studies
D-optimal plans in observational studies Constanze Pumplün Stefan Rüping Katharina Morik Claus Weihs October 11, 2005 Abstract This paper investigates the use of Design of Experiments in observational
More informationOnline Semi-Supervised Learning
Online Semi-Supervised Learning Andrew B. Goldberg, Ming Li, Xiaojin Zhu jerryzhu@cs.wisc.edu Computer Sciences University of Wisconsin Madison Xiaojin Zhu (Univ. Wisconsin-Madison) Online Semi-Supervised
More informationMining Direct Marketing Data by Ensembles of Weak Learners and Rough Set Methods
Mining Direct Marketing Data by Ensembles of Weak Learners and Rough Set Methods Jerzy B laszczyński 1, Krzysztof Dembczyński 1, Wojciech Kot lowski 1, and Mariusz Paw lowski 2 1 Institute of Computing
More informationLinear 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 informationFoundations of Machine Learning On-Line Learning. Mehryar Mohri Courant Institute and Google Research mohri@cims.nyu.edu
Foundations of Machine Learning On-Line Learning Mehryar Mohri Courant Institute and Google Research mohri@cims.nyu.edu Motivation PAC learning: distribution fixed over time (training and test). IID assumption.
More informationContent-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 informationDatabase Marketing, Business Intelligence and Knowledge Discovery
Database Marketing, Business Intelligence and Knowledge Discovery Note: Using material from Tan / Steinbach / Kumar (2005) Introduction to Data Mining,, Addison Wesley; and Cios / Pedrycz / Swiniarski
More informationBig 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 informationDistance 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 informationKnowledge Discovery from patents using KMX Text Analytics
Knowledge Discovery from patents using KMX Text Analytics Dr. Anton Heijs anton.heijs@treparel.com Treparel Abstract In this white paper we discuss how the KMX technology of Treparel can help searchers
More informationBayesian 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 informationNetwork Big Data: Facing and Tackling the Complexities Xiaolong Jin
Network Big Data: Facing and Tackling the Complexities Xiaolong Jin CAS Key Laboratory of Network Data Science & Technology Institute of Computing Technology Chinese Academy of Sciences (CAS) 2015-08-10
More informationChapter 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 informationTable 1: Summary of the settings and parameters employed by the additive PA algorithm for classification, regression, and uniclass.
Online Passive-Aggressive Algorithms Koby Crammer Ofer Dekel Shai Shalev-Shwartz Yoram Singer School of Computer Science & Engineering The Hebrew University, Jerusalem 91904, Israel {kobics,oferd,shais,singer}@cs.huji.ac.il
More informationBIOINF 585 Fall 2015 Machine Learning for Systems Biology & Clinical Informatics http://www.ccmb.med.umich.edu/node/1376
Course Director: Dr. Kayvan Najarian (DCM&B, kayvan@umich.edu) Lectures: Labs: Mondays and Wednesdays 9:00 AM -10:30 AM Rm. 2065 Palmer Commons Bldg. Wednesdays 10:30 AM 11:30 AM (alternate weeks) Rm.
More informationMapReduce/Bigtable for Distributed Optimization
MapReduce/Bigtable for Distributed Optimization Keith B. Hall Google Inc. kbhall@google.com Scott Gilpin Google Inc. sgilpin@google.com Gideon Mann Google Inc. gmann@google.com Abstract With large data
More informationCollaborative Filtering. Radek Pelánek
Collaborative Filtering Radek Pelánek 2015 Collaborative Filtering assumption: users with similar taste in past will have similar taste in future requires only matrix of ratings applicable in many domains
More informationPredicting 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 informationSURVEY REPORT DATA SCIENCE SOCIETY 2014
SURVEY REPORT DATA SCIENCE SOCIETY 2014 TABLE OF CONTENTS Contents About the Initiative 1 Report Summary 2 Participants Info 3 Participants Expertise 6 Suggested Discussion Topics 7 Selected Responses
More informationIns+tuto Superior Técnico Technical University of Lisbon. Big Data. Bruno Lopes Catarina Moreira João Pinho
Ins+tuto Superior Técnico Technical University of Lisbon Big Data Bruno Lopes Catarina Moreira João Pinho Mo#va#on 2 220 PetaBytes Of data that people create every day! 2 Mo#va#on 90 % of Data UNSTRUCTURED
More informationChallenges and Lessons from NIST Data Science Pre-pilot Evaluation in Introduction to Data Science Course Fall 2015
Challenges and Lessons from NIST Data Science Pre-pilot Evaluation in Introduction to Data Science Course Fall 2015 Dr. Daisy Zhe Wang Director of Data Science Research Lab University of Florida, CISE
More informationLarge-Scale Similarity and Distance Metric Learning
Large-Scale Similarity and Distance Metric Learning Aurélien Bellet Télécom ParisTech Joint work with K. Liu, Y. Shi and F. Sha (USC), S. Clémençon and I. Colin (Télécom ParisTech) Séminaire Criteo March
More informationAnalytics on Big Data
Analytics on Big Data Riccardo Torlone Università Roma Tre Credits: Mohamed Eltabakh (WPI) Analytics The discovery and communication of meaningful patterns in data (Wikipedia) It relies on data analysis
More informationDATA 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 informationPrediction of Stock Performance Using Analytical Techniques
136 JOURNAL OF EMERGING TECHNOLOGIES IN WEB INTELLIGENCE, VOL. 5, NO. 2, MAY 2013 Prediction of Stock Performance Using Analytical Techniques Carol Hargreaves Institute of Systems Science National University
More informationSpark and the Big Data Library
Spark and the Big Data Library Reza Zadeh Thanks to Matei Zaharia Problem Data growing faster than processing speeds Only solution is to parallelize on large clusters» Wide use in both enterprises and
More informationBig 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 informationOnline Kernel Selection: Algorithms and Evaluations
Proceedings of the Twenty-Sixth AAAI Conference on Artificial Intelligence Online Kernel Selection: Algorithms and Evaluations Tianbao Yang 1, Mehrdad Mahdavi 1, Rong Jin 1, Jinfeng Yi 1, Steven C. H.
More informationBig Data Analytics. An Introduction. Oliver Fuchsberger University of Paderborn 2014
Big Data Analytics An Introduction Oliver Fuchsberger University of Paderborn 2014 Table of Contents I. Introduction & Motivation What is Big Data Analytics? Why is it so important? II. Techniques & Solutions
More informationREVIEW OF ENSEMBLE CLASSIFICATION
Available Online at www.ijcsmc.com International Journal of Computer Science and Mobile Computing A Monthly Journal of Computer Science and Information Technology ISSN 2320 088X IJCSMC, Vol. 2, Issue.
More informationJubatus: An Open Source Platform for Distributed Online Machine Learning
Jubatus: An Open Source Platform for Distributed Online Machine Learning Shohei Hido Seiya Tokui Preferred Infrastructure Inc. Tokyo, Japan {hido, tokui}@preferred.jp Satoshi Oda NTT Software Innovation
More informationBig Data Analytic and Mining with Machine Learning Algorithm
International Journal of Information and Computation Technology. ISSN 0974-2239 Volume 4, Number 1 (2014), pp. 33-40 International Research Publications House http://www. irphouse.com /ijict.htm Big Data
More informationBig-data Analytics: Challenges and Opportunities
Big-data Analytics: Challenges and Opportunities Chih-Jen Lin Department of Computer Science National Taiwan University Talk at 台 灣 資 料 科 學 愛 好 者 年 會, August 30, 2014 Chih-Jen Lin (National Taiwan Univ.)
More informationVisual Data Mining. Motivation. Why Visual Data Mining. Integration of visualization and data mining : Chidroop Madhavarapu CSE 591:Visual Analytics
Motivation Visual Data Mining Visualization for Data Mining Huge amounts of information Limited display capacity of output devices Chidroop Madhavarapu CSE 591:Visual Analytics Visual Data Mining (VDM)
More informationLearning to Rank Revisited: Our Progresses in New Algorithms and Tasks
The 4 th China-Australia Database Workshop Melbourne, Australia Oct. 19, 2015 Learning to Rank Revisited: Our Progresses in New Algorithms and Tasks Jun Xu Institute of Computing Technology, Chinese Academy
More informationHYBRID PROBABILITY BASED ENSEMBLES FOR BANKRUPTCY PREDICTION
HYBRID PROBABILITY BASED ENSEMBLES FOR BANKRUPTCY PREDICTION Chihli Hung 1, Jing Hong Chen 2, Stefan Wermter 3, 1,2 Department of Management Information Systems, Chung Yuan Christian University, Taiwan
More informationHybrid model rating prediction with Linked Open Data for Recommender Systems
Hybrid model rating prediction with Linked Open Data for Recommender Systems Andrés Moreno 12 Christian Ariza-Porras 1, Paula Lago 1, Claudia Jiménez-Guarín 1, Harold Castro 1, and Michel Riveill 2 1 School
More informationCLASSIFYING NETWORK TRAFFIC IN THE BIG DATA ERA
CLASSIFYING NETWORK TRAFFIC IN THE BIG DATA ERA Professor Yang Xiang Network Security and Computing Laboratory (NSCLab) School of Information Technology Deakin University, Melbourne, Australia http://anss.org.au/nsclab
More informationInternational Journal of Engineering Research ISSN: 2348-4039 & Management Technology November-2015 Volume 2, Issue-6
International Journal of Engineering Research ISSN: 2348-4039 & Management Technology Email: editor@ijermt.org November-2015 Volume 2, Issue-6 www.ijermt.org Modeling Big Data Characteristics for Discovering
More informationClass Imbalance Learning in Software Defect Prediction
Class Imbalance Learning in Software Defect Prediction Dr. Shuo Wang s.wang@cs.bham.ac.uk University of Birmingham Research keywords: ensemble learning, class imbalance learning, online learning Shuo Wang
More informationChallenges for Data Driven Systems
Challenges for Data Driven Systems Eiko Yoneki University of Cambridge Computer Laboratory Quick History of Data Management 4000 B C Manual recording From tablets to papyrus to paper A. Payberah 2014 2
More informationBIG 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 informationA Survey of Classification Techniques in the Area of Big Data.
A Survey of Classification Techniques in the Area of Big Data. 1PrafulKoturwar, 2 SheetalGirase, 3 Debajyoti Mukhopadhyay 1Reseach Scholar, Department of Information Technology 2Assistance Professor,Department
More information