Basic Pattern Recognition Concept


 Kathlyn Phillips
 11 months ago
 Views:
Transcription
1 Concepts of Pattern Basic Pattern Concept Xiaojun Qi Pattern: A pattern is the description of an object. According to the nature of the patterns to be recognized, we may divide our acts of recognition into two major types: The recognition of concrete items The recognition of abstract items When a person perceives a pattern, he makes an inductive inference and associates this perception with some general concepts or clues which he has derived from his past experience. Thus, the problem of pattern recognition may be regarded as one of discriminating of the input data, not between individual patterns but between populations, via the search for features or invariant attributes among members of a population. The study of pattern recognition problems may be logically divided into two major categories: The study of the pattern recognition capability of human beings and other living organisms. (Psychology, Physiology, and Biology) The development of theory and techniques for the design of devices capable of performing a given recognition task for a specific application. (Engineering, Computer, and Information Science) Task of Classification Input Data Output Response Pattern recognition can be defined as the categorization of input data into identifiable classes via the extraction of significant features or attributes of the data from a background of irrelevant detail. Character Speech Speaker Weather Prediction Optical signals or strokes Acoustic waveforms Voice Weather maps Name of character Name of word Name of speaker Weather forecast Medical Diagnosis Symptoms Disease Stock Market Prediction Financial news and charts Predicted market ups and downs.
2 Pattern Class: It is a category determined by some given common attributes. Pattern: It is the description of any member of a category representing a pattern class. When a set of patterns falling into disjoint classes is available, it is desired to categorize these patterns into their respective classes through the use of some automatic device. The basic functions of a pattern recognition system are to detect and extract common features from the patterns describing the objects that belong to the same pattern class, and to recognize this pattern in any new environment and classify it as a member of one of the pattern classes under consideration. Fundamental Problems in Pattern System Design The first one is concerned with the representation of input data which can be measured from the objects to be recognized. The pattern vectors contain all the measured information available about the patterns. The measurements performed on the objects of a pattern class may be regarded as a coding process which consists of assigning to each pattern characteristic a symbol from the alphabet set. When the measurements yield information in the form of real numbers, it is often useful to think of a pattern vector as a point in an ndimensional Euclidean space. The set of patterns belonging to the same class corresponds to an ensemble of points scattered within some region of the measurement space. The second problem concerns the extraction of characteristic features or attributes from the received input data and the reduction of the dimensionality of pattern vectors. (This is often referred to as the preprocessing and feature extraction problem.) The features of a pattern class are the characterizing attributes common to all patterns belonging to that class. Such features are often referred to as intraset features. The features which represent the differences between pattern classes may be referred to as the interset features. The elements of intraset features which are common to all pattern classes under consideration carry no discriminatory information and can be ignored. The extraction of features has been recognized as an important problem in the design of pattern recognition systems. The third problem involves the determination of optimum decision procedures, which are needed in the identification and classification process. If completed a prior knowledge about the patterns to be recognized is available, the decision functions may be determined with precision on the basis of this information. If only qualitative knowledge about the patterns is available, reasonable guesses of the forms of the decision functions can be made. Need adjustment as necessary. If there exists little, if any, a priori knowledge about the patterns to be recognized, a training or learning procedure is needed. The patterns to be recognized and classified by an automatic pattern recognition system must possess a set of measurable characteristics. Correct recognition will depend on The amount of discriminating information contained in the measurements; The effective utilization of this information. Design Concepts and Methodologies Membershiproster Concept Characterization of a pattern class by a roster of its members suggests automatic pattern recognition by template matching. The membershiproster approach will work satisfactorily under the condition of nearly perfect pattern samples.
3 Commonproperty Concept Characterization of a pattern class by common properties shared by all of its members suggests automatic pattern recognition via the detection and processing of similar features. The basic assumption in this method is that the patterns belonging to the same class possess certain common properties or attributes which reflect similarities among these patterns. Advantage: (Membershiproster Concept vs. Commonproperty Concept) The storage requirement for the features of a pattern class is much less severe than that for all the patterns in the class. Significant pattern variations cannot be tolerated in template matching. If all the features of a class can be determined from sample patterns, the recognition process reduces simply to feature matching. Clustering Concept When the patterns of a class are vectors whose components are real numbers, a pattern class can be characterized by its clustering properties in the pattern space. If the classes are characterized by clusters which are far apart, simple recognition schemes such as the minimumdistance classifiers may be successfully employed. When the clusters overlap, it becomes necessary to utilize more sophisticated techniques for partitioning the pattern space. Overlapping clusters are the result of: A deficiency in observed information; The presence of measurement noise. The degree of overlapping can often be minimized by: Increasing the number and the quality of measurements performed on the patterns of a class. The basic design concepts for automatic pattern recognition described above may be implemented by three principal categories of methodology: Heuristic; Mathematical; Linguistic or syntactic. Heuristic Methods: The heuristic approach is based on human intuition and experience, making use of the membershiproster and commonproperty concepts. A system designed using this principle generally consists of a set of ad hoc procedures developed for specialized recognition tasks. Decision is based on ad hoc rules. Example: Character recognition (Detection of features such as the number and sequence of particular strokes)
4 Mathematical Methods: It is based on classification rules which are formulated and derived in a mathematical framework, making use of the commonproperty and clustering concepts. Deterministic approach: Does not employ explicitly the statistical properties of the pattern classes. Statistical approach: It is formulated and derived in a statistical framework. Example: Bayes classification rule and its variations. This rule yields an optimum classifier when the probability density function of each pattern population and the probability of occurrence of each pattern class are known. Linguistic (Syntactic) Methods: Characterization of patterns by primitive elements (subpatterns) and their relationships suggests automatic pattern recognition by the linguistic or syntactic approach, making use of the commonproperty concept. A pattern can be described by a hierarchical structure of subpatterns analogous to the syntactic structure of languages. This permits application of formal language theory to the pattern recognition problem. This approach is particularly useful in dealing with patterns which cannot be conveniently described by numerical measurements or are so complex that local features cannot be identified and global properties must be used. In a supervised learning environment, the system is taught to recognize patterns by means of various adaptive schemes. The essentials of this approach are a set of training patterns of known classification and the implementation of an appropriate learning procedure. The unsupervised pattern recognition techniques are applicable to the situations where only a set of training patterns of unknown classification may be available. Examples of Automatic Pattern Systems Character : Technique Used: Rather than being compared with prestored patterns, handprinted characters are analyzed as combinations of common features, such as curved lines, vertical and horizontal lines, corners, and intersections. Automatic Classification of Remotely Sensed Data: Examples: Land use, crop inventory, cropdisease detection, forestry, monitoring of air and water quality, geological and geographical studies, and weather prediction, plus a score of other applications of environmental significance. Technique Used: Bayes classifier Biomedical Applications: Technique Used: Pattern primitives, such as long arcs, short arcs, and semistraight segments, which characterize the chromosome boundaries are defined. When combined, these primitives form a string or symbol sentence which can be associated with a socalled pattern grammar. There is one grammar for each type (class) of chromosome.
5 Fingerprint : Technique Used: It detects tentative minutiae and records their precise locations and angles. Nuclear Reactor Component Surveillance: Technique Used: Detect the clusters of pattern vectors by iterative applications of a clusterseeking algorithm. The data cluster centers and associated descriptive parameters, such as cluster variances, can then be used as templates against which measurements are compared at any given time in order to determine the status of the plants. Significant deviations from the preestablished characteristic normal behavior are flagged as indications of an abnormal operating conditions. A Simple Pattern Model A simple scheme for pattern recognition consists of two basic components: Sensor: It is a device which converts a physical sample to be recognized into a set of quantities which characterize the sample. Categorizer: It is a device which assigns each of its admissible inputs to one of a finite number of classes or categories by computing a set of decision functions. We assume that the a priori probabilities for the occurrence of each class are equal, that is, it is just as likely that x comes from one class as from another.
Introduction to Pattern Recognition
Introduction to Pattern Recognition Selim Aksoy Department of Computer Engineering Bilkent University saksoy@cs.bilkent.edu.tr CS 551, Spring 2009 CS 551, Spring 2009 c 2009, Selim Aksoy (Bilkent University)
More informationMachine Learning CS 6830. Lecture 01. Razvan C. Bunescu School of Electrical Engineering and Computer Science bunescu@ohio.edu
Machine Learning CS 6830 Razvan C. Bunescu School of Electrical Engineering and Computer Science bunescu@ohio.edu What is Learning? MerriamWebster: learn = to acquire knowledge, understanding, or skill
More information. Learn the number of classes and the structure of each class using similarity between unlabeled training patterns
Outline Part 1: of data clustering NonSupervised Learning and Clustering : Problem formulation cluster analysis : Taxonomies of Clustering Techniques : Data types and Proximity Measures : Difficulties
More informationLecture 20: Clustering
Lecture 20: Clustering Wrapup of neural nets (from last lecture Introduction to unsupervised learning Kmeans clustering COMP424, Lecture 20  April 3, 2013 1 Unsupervised learning In supervised learning,
More informationAn Enhanced Clustering Algorithm to Analyze Spatial Data
International Journal of Engineering and Technical Research (IJETR) ISSN: 23210869, Volume2, Issue7, July 2014 An Enhanced Clustering Algorithm to Analyze Spatial Data Dr. Mahesh Kumar, Mr. Sachin Yadav
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 informationEnvironmental Remote Sensing GEOG 2021
Environmental Remote Sensing GEOG 2021 Lecture 4 Image classification 2 Purpose categorising data data abstraction / simplification data interpretation mapping for land cover mapping use land cover class
More informationA Matlab Project in Optical Character Recognition (OCR)
A Matlab Project in Optical Character Recognition (OCR) Jesse Hansen Introduction: What is OCR? The goal of Optical Character Recognition (OCR) is to classify optical patterns (often contained in a digital
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 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 informationSUCCESSFUL PREDICTION OF HORSE RACING RESULTS USING A NEURAL NETWORK
SUCCESSFUL PREDICTION OF HORSE RACING RESULTS USING A NEURAL NETWORK N M Allinson and D Merritt 1 Introduction This contribution has two main sections. The first discusses some aspects of multilayer perceptrons,
More informationSTATISTICA. Clustering Techniques. Case Study: Defining Clusters of Shopping Center Patrons. and
Clustering Techniques and STATISTICA Case Study: Defining Clusters of Shopping Center Patrons STATISTICA Solutions for Business Intelligence, Data Mining, Quality Control, and Webbased Analytics Table
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 informationMachine Learning: Overview
Machine Learning: Overview Why Learning? Learning is a core of property of being intelligent. Hence Machine learning is a core subarea of Artificial Intelligence. There is a need for programs to behave
More informationLearning Example. Machine learning and our focus. Another Example. An example: data (loan application) The data and the goal
Learning Example Chapter 18: Learning from Examples 22c:145 An emergency room in a hospital measures 17 variables (e.g., blood pressure, age, etc) of newly admitted patients. A decision is needed: whether
More informationOUTLIER ANALYSIS. Data Mining 1
OUTLIER ANALYSIS Data Mining 1 What Are Outliers? Outlier: A data object that deviates significantly from the normal objects as if it were generated by a different mechanism Ex.: Unusual credit card purchase,
More informationSocial 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 informationInternational Journal of Electronics and Computer Science Engineering 1449
International Journal of Electronics and Computer Science Engineering 1449 Available Online at www.ijecse.org ISSN 22771956 Neural Networks in Data Mining Priyanka Gaur Department of Information and
More informationARTIFICIAL INTELLIGENCE (CSCU9YE) LECTURE 6: MACHINE LEARNING 2: UNSUPERVISED LEARNING (CLUSTERING)
ARTIFICIAL INTELLIGENCE (CSCU9YE) LECTURE 6: MACHINE LEARNING 2: UNSUPERVISED LEARNING (CLUSTERING) Gabriela Ochoa http://www.cs.stir.ac.uk/~goc/ OUTLINE Preliminaries Classification and Clustering Applications
More informationAdditional Case Study Two: Property Risk Assessment: A Simulation Approach
Additional Case Study Two: Property Risk Assessment: A Simulation Approach This case study focuses on analyzing the risk of investing in income properties. The key point is that measures of central tendency,
More informationData Mining. Practical Machine Learning Tools and Techniques. Classification, association, clustering, numeric prediction
Data Mining Practical Machine Learning Tools and Techniques Slides for Chapter 2 of Data Mining by I. H. Witten and E. Frank Input: Concepts, instances, attributes Terminology What s a concept? Classification,
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 informationClustering Connectionist and Statistical Language Processing
Clustering Connectionist and Statistical Language Processing Frank Keller keller@coli.unisb.de Computerlinguistik Universität des Saarlandes Clustering p.1/21 Overview clustering vs. classification supervised
More informationAn Arabic TextToSpeech System Based on Artificial Neural Networks
Journal of Computer Science 5 (3): 207213, 2009 ISSN 15493636 2009 Science Publications An Arabic TextToSpeech System Based on Artificial Neural Networks Ghadeer AlSaid and Moussa Abdallah Department
More informationUniversité de Montpellier 2 Hugo AlatristaSalas : hugo.alatristasalas@teledetection.fr
Université de Montpellier 2 Hugo AlatristaSalas : hugo.alatristasalas@teledetection.fr WEKA Gallirallus Zeland) australis : Endemic bird (New Characteristics Waikato university Weka is a collection
More informationStudents will understand 1. use numerical bases and the laws of exponents
Grade 8 Expressions and Equations Essential Questions: 1. How do you use patterns to understand mathematics and model situations? 2. What is algebra? 3. How are the horizontal and vertical axes related?
More informationData Mining Part 5. Prediction
Data Mining Part 5. Prediction 5.1 Spring 2010 Instructor: Dr. Masoud Yaghini Outline Classification vs. Numeric Prediction Prediction Process Data Preparation Comparing Prediction Methods References Classification
More informationModelling, Extraction and Description of Intrinsic Cues of High Resolution Satellite Images: Independent Component Analysis based approaches
Modelling, Extraction and Description of Intrinsic Cues of High Resolution Satellite Images: Independent Component Analysis based approaches PhD Thesis by Payam Birjandi Director: Prof. Mihai Datcu Problematic
More informationData Mining and Knowledge Discovery in Databases (KDD) State of the Art. Prof. Dr. T. Nouri Computer Science Department FHNW Switzerland
Data Mining and Knowledge Discovery in Databases (KDD) State of the Art Prof. Dr. T. Nouri Computer Science Department FHNW Switzerland 1 Conference overview 1. Overview of KDD and data mining 2. Data
More informationOverview. Clustering. Clustering vs. Classification. Supervised vs. Unsupervised Learning. Connectionist and Statistical Language Processing
Overview Clustering Connectionist and Statistical Language Processing Frank Keller keller@coli.unisb.de Computerlinguistik Universität des Saarlandes clustering vs. classification supervised vs. unsupervised
More informationGraduate Coop Students Information Manual. Department of Computer Science. Faculty of Science. University of Regina
Graduate Coop Students Information Manual Department of Computer Science Faculty of Science University of Regina 2014 1 Table of Contents 1. Department Description..3 2. Program Requirements and Procedures
More informationCS 2750 Machine Learning. Lecture 1. Machine Learning. http://www.cs.pitt.edu/~milos/courses/cs2750/ CS 2750 Machine Learning.
Lecture Machine Learning Milos Hauskrecht milos@cs.pitt.edu 539 Sennott Square, x5 http://www.cs.pitt.edu/~milos/courses/cs75/ Administration Instructor: Milos Hauskrecht milos@cs.pitt.edu 539 Sennott
More informationDocument Image Retrieval using Signatures as Queries
Document Image Retrieval using Signatures as Queries Sargur N. Srihari, Shravya Shetty, Siyuan Chen, Harish Srinivasan, Chen Huang CEDAR, University at Buffalo(SUNY) Amherst, New York 14228 Gady Agam and
More informationOutlier 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 informationChapter 12 Discovering New Knowledge Data Mining
Chapter 12 Discovering New Knowledge Data Mining BecerraFernandez, et al.  Knowledge Management 1/e  2004 Prentice Hall Additional material 2007 Dekai Wu Chapter Objectives Introduce the student to
More informationMultisensor Data Fusion and Applications
Multisensor Data Fusion and Applications Pramod K. Varshney Department of Electrical Engineering and Computer Science Syracuse University 121 Link Hall Syracuse, New York 13244 USA Email: varshney@syr.edu
More informationIntroduction to Statistical Machine Learning
CHAPTER Introduction to Statistical Machine Learning We start with a gentle introduction to statistical machine learning. Readers familiar with machine learning may wish to skip directly to Section 2,
More informationPrinciples of Data Mining by Hand&Mannila&Smyth
Principles of Data Mining by Hand&Mannila&Smyth Slides for Textbook Ari Visa,, Institute of Signal Processing Tampere University of Technology October 4, 2010 Data Mining: Concepts and Techniques 1 Differences
More informationMonotonicity Hints. Abstract
Monotonicity Hints Joseph Sill Computation and Neural Systems program California Institute of Technology email: joe@cs.caltech.edu Yaser S. AbuMostafa EE and CS Deptartments California Institute of Technology
More informationIntroduction 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 informationINTRODUCTION TO MACHINE LEARNING
Why are you here? What is Machine Learning? Why are you taking this course? INTRODUCTION TO MACHINE LEARNING David Kauchak CS 451 Fall 2013 What topics would you like to see covered? Machine Learning is
More informationThe Data Mining Process
Sequence for Determining Necessary Data. Wrong: Catalog everything you have, and decide what data is important. Right: Work backward from the solution, define the problem explicitly, and map out the data
More informationWord Spotting in Cursive Handwritten Documents using Modified Character Shape Codes
Word Spotting in Cursive Handwritten Documents using Modified Character Shape Codes Sayantan Sarkar Department of Electrical Engineering, NIT Rourkela sayantansarkar24@gmail.com Abstract.There is a large
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 informationUsing Lexical Similarity in Handwritten Word Recognition
Using Lexical Similarity in Handwritten Word Recognition Jaehwa Park and Venu Govindaraju Center of Excellence for Document Analysis and Recognition (CEDAR) Department of Computer Science and Engineering
More informationUse of Data Mining Techniques to Improve the Effectiveness of Sales and Marketing
Available Online at www.ijcsmc.com International Journal of Computer Science and Mobile Computing A Monthly Journal of Computer Science and Information Technology IJCSMC, Vol. 4, Issue. 4, April 2015,
More informationData Mining  Evaluation of Classifiers
Data Mining  Evaluation of Classifiers Lecturer: JERZY STEFANOWSKI Institute of Computing Sciences Poznan University of Technology Poznan, Poland Lecture 4 SE Master Course 2008/2009 revised for 2010
More information1311. Adding New Level in KDD to Make the Web Usage Mining More Efficient. Abstract. 1. Introduction [1]. 1/10
1/10 1311 Adding New Level in KDD to Make the Web Usage Mining More Efficient Mohammad Ala a AL_Hamami PHD Student, Lecturer m_ah_1@yahoocom Soukaena Hassan Hashem PHD Student, Lecturer soukaena_hassan@yahoocom
More informationRisk Analysis and Quantification
Risk Analysis and Quantification 1 What is Risk Analysis? 2. Risk Analysis Methods 3. The Monte Carlo Method 4. Risk Model 5. What steps must be taken for the development of a Risk Model? 1.What is Risk
More informationMore Local Structure Information for MakeModel Recognition
More Local Structure Information for MakeModel Recognition David Anthony Torres Dept. of Computer Science The University of California at San Diego La Jolla, CA 9093 Abstract An object classification
More informationBEHAVIOR BASED CREDIT CARD FRAUD DETECTION USING SUPPORT VECTOR MACHINES
BEHAVIOR BASED CREDIT CARD FRAUD DETECTION USING SUPPORT VECTOR MACHINES 123 CHAPTER 7 BEHAVIOR BASED CREDIT CARD FRAUD DETECTION USING SUPPORT VECTOR MACHINES 7.1 Introduction Even though using SVM presents
More informationFacial Expression Recognition using a Dynamic Model and Motion Energy
Facial Expression Recognition using a Dynamic Model and Motion Energy Irfan Essa, Alex Pentland (a review by Paul Fitzpatrick for 6.892) Overview Want to categorize facial motion Existing coding schemes
More informationCHAPTER24 Mining Spatial Databases
CHAPTER24 Mining Spatial Databases 24.1 Introduction 24.2 Spatial Data Cube Construction and Spatial OLAP 24.3 Spatial Association Analysis 24.4 Spatial Clustering Methods 24.5 Spatial Classification
More informationData Clustering. Dec 2nd, 2013 Kyrylo Bessonov
Data Clustering Dec 2nd, 2013 Kyrylo Bessonov Talk outline Introduction to clustering Types of clustering Supervised Unsupervised Similarity measures Main clustering algorithms kmeans Hierarchical Main
More informationAn Introduction to Data Mining
An Introduction to Intel Beijing wei.heng@intel.com January 17, 2014 Outline 1 DW Overview What is Notable Application of Conference, Software and Applications Major Process in 2 Major Tasks in Detail
More informationIndex Contents Page No. Introduction . Data Mining & Knowledge Discovery
Index Contents Page No. 1. Introduction 1 1.1 Related Research 2 1.2 Objective of Research Work 3 1.3 Why Data Mining is Important 3 1.4 Research Methodology 4 1.5 Research Hypothesis 4 1.6 Scope 5 2.
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 informationEFFICIENT DATA PREPROCESSING FOR DATA MINING
EFFICIENT DATA PREPROCESSING FOR DATA MINING USING NEURAL NETWORKS JothiKumar.R 1, Sivabalan.R.V 2 1 Research scholar, Noorul Islam University, Nagercoil, India Assistant Professor, Adhiparasakthi College
More informationComparison of Nonlinear Dimensionality Reduction Techniques for Classification with Gene Expression Microarray Data
CMPE 59H Comparison of Nonlinear Dimensionality Reduction Techniques for Classification with Gene Expression Microarray Data Term Project Report Fatma Güney, Kübra Kalkan 1/15/2013 Keywords: Nonlinear
More informationA FUZZY BASED APPROACH TO TEXT MINING AND DOCUMENT CLUSTERING
A FUZZY BASED APPROACH TO TEXT MINING AND DOCUMENT CLUSTERING Sumit Goswami 1 and Mayank Singh Shishodia 2 1 Indian Institute of TechnologyKharagpur, Kharagpur, India sumit_13@yahoo.com 2 School of Computer
More informationIntroduction to machine learning and pattern recognition Lecture 1 Coryn BailerJones
Introduction to machine learning and pattern recognition Lecture 1 Coryn BailerJones http://www.mpia.de/homes/calj/mlpr_mpia2008.html 1 1 What is machine learning? Data description and interpretation
More informationSearch and Data Mining: Techniques. Text Mining Anya Yarygina Boris Novikov
Search and Data Mining: Techniques Text Mining Anya Yarygina Boris Novikov Introduction Generally used to denote any system that analyzes large quantities of natural language text and detects lexical or
More informationKnowledge Maps and Mathematical Modelling
Knowledge Maps and Mathematical Modelling Tomas Subrt and Helena Brozova University of Life Sciences Prague, Czech Republic Subrt@pef.czu.cz Brozova@pef.czu.cz Abstract: The aim of our paper is to explain
More informationSpatial Data Analysis
14 Spatial Data Analysis OVERVIEW This chapter is the first in a set of three dealing with geographic analysis and modeling methods. The chapter begins with a review of the relevant terms, and an outlines
More informationBig Data: Rethinking Text Visualization
Big Data: Rethinking Text Visualization Dr. Anton Heijs anton.heijs@treparel.com Treparel April 8, 2013 Abstract In this white paper we discuss text visualization approaches and how these are important
More informationClassification of Fingerprints. Sarat C. Dass Department of Statistics & Probability
Classification of Fingerprints Sarat C. Dass Department of Statistics & Probability Fingerprint Classification Fingerprint classification is a coarse level partitioning of a fingerprint database into smaller
More informationImproved Fuzzy Cmeans Clustering Algorithm Based on Cluster Density
Journal of Computational Information Systems 8: 2 (2012) 727 737 Available at http://www.jofcis.com Improved Fuzzy Cmeans Clustering Algorithm Based on Cluster Density Xiaojun LOU, Junying LI, Haitao
More informationCarroll County Public Schools Elementary Mathematics Instructional Guide (5 th Grade) AugustSeptember (12 days) Unit #1 : Geometry
Carroll County Public Schools Elementary Mathematics Instructional Guide (5 th Grade) Common Core and Research from the CCSS Progression Documents Geometry Students learn to analyze and relate categories
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 informationRegression Using Support Vector Machines: Basic Foundations
Regression Using Support Vector Machines: Basic Foundations Technical Report December 2004 Aly Farag and Refaat M Mohamed Computer Vision and Image Processing Laboratory Electrical and Computer Engineering
More informationUsing 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 information1 What is Machine Learning?
COS 511: Theoretical Machine Learning Lecturer: Rob Schapire Lecture #1 Scribe: Rob Schapire February 4, 2008 1 What is Machine Learning? Machine learning studies computer algorithms for learning to do
More informationLearning outcomes. Knowledge and understanding. Ability and Competences. Evaluation capability and scientific approach
Syllabus Master s Programme in Statistics and Data Mining 120 ECTS Credits Aim The rapid growth of databases provides scientists and business people with vast new resources. This programme meets the challenges
More informationMaschinelles Lernen mit MATLAB
Maschinelles Lernen mit MATLAB Jérémy Huard Applikationsingenieur The MathWorks GmbH 2015 The MathWorks, Inc. 1 Machine Learning is Everywhere Image Recognition Speech Recognition Stock Prediction Medical
More informationData Mining Practical Machine Learning Tools and Techniques
Data ining Practical achine Learning Tools and Techniques Slides for Chapter 2 of Data ining by I. H. Witten and E. rank Outline Terminology What s a concept Classification, association, clustering, numeric
More informationTime Series Analysis Using Unsupervised Construction of Hierarchical Classifiers
From: FLAIRS Proceedings. Copyright, AAAI (www.aaai.org). All rights reserved. Time Series Analysis Using Unsupervised Construction of Hierarchical Classifiers S.A.Dolenko, Yu.V.Orlov, I.G.Persiantsev,
More informationImage Estimation Algorithm for Out of Focus and Blur Images to Retrieve the Barcode Value
IJSTE  International Journal of Science Technology & Engineering Volume 1 Issue 10 April 2015 ISSN (online): 2349784X Image Estimation Algorithm for Out of Focus and Blur Images to Retrieve the Barcode
More informationUsing Data Mining for Mobile Communication Clustering and Characterization
Using Data Mining for Mobile Communication Clustering and Characterization A. Bascacov *, C. Cernazanu ** and M. Marcu ** * Lasting Software, Timisoara, Romania ** Politehnica University of Timisoara/Computer
More informationMachine Learning for Data Science (CS4786) Lecture 1
Machine Learning for Data Science (CS4786) Lecture 1 TuTh 10:10 to 11:25 AM Hollister B14 Instructors : Lillian Lee and Karthik Sridharan ROUGH DETAILS ABOUT THE COURSE Diagnostic assignment 0 is out:
More informationMachine Learning with MATLAB David Willingham Application Engineer
Machine Learning with MATLAB David Willingham Application Engineer 2014 The MathWorks, Inc. 1 Goals Overview of machine learning Machine learning models & techniques available in MATLAB Streamlining the
More informationMachine Learning. Chapter 18, 21. Some material adopted from notes by Chuck Dyer
Machine Learning Chapter 18, 21 Some material adopted from notes by Chuck Dyer What is learning? Learning denotes changes in a system that... enable a system to do the same task more efficiently the next
More informationIntroduction to Learning & Decision Trees
Artificial Intelligence: Representation and Problem Solving 538 April 0, 2007 Introduction to Learning & Decision Trees Learning and Decision Trees to learning What is learning?  more than just memorizing
More informationArtificial Neural Network for Speech Recognition
Artificial Neural Network for Speech Recognition Austin Marshall March 3, 2005 2nd Annual Student Research Showcase Overview Presenting an Artificial Neural Network to recognize and classify speech Spoken
More informationDYNAMIC FUZZY PATTERN RECOGNITION WITH APPLICATIONS TO FINANCE AND ENGINEERING LARISA ANGSTENBERGER
DYNAMIC FUZZY PATTERN RECOGNITION WITH APPLICATIONS TO FINANCE AND ENGINEERING LARISA ANGSTENBERGER Kluwer Academic Publishers Boston/Dordrecht/London TABLE OF CONTENTS FOREWORD ACKNOWLEDGEMENTS XIX XXI
More informationGuido Sciavicco. 11 Novembre 2015
classical and new techniques Università degli Studi di Ferrara 11 Novembre 2015 in collaboration with dr. Enrico Marzano, CIO Gap srl Active Contact System Project 1/27 Contents What is? Embedded Wrapper
More informationLecture 9: Introduction to Pattern Analysis
Lecture 9: Introduction to Pattern Analysis g Features, patterns and classifiers g Components of a PR system g An example g Probability definitions g Bayes Theorem g Gaussian densities Features, patterns
More informationNAVIGATING SCIENTIFIC LITERATURE A HOLISTIC PERSPECTIVE. Venu Govindaraju
NAVIGATING SCIENTIFIC LITERATURE A HOLISTIC PERSPECTIVE Venu Govindaraju BIOMETRICS DOCUMENT ANALYSIS PATTERN RECOGNITION 8/24/2015 ICDAR 2015 2 Towards a Globally Optimal Approach for Learning Deep Unsupervised
More informationConcepts of digital forensics
Chapter 3 Concepts of digital forensics Digital forensics is a branch of forensic science concerned with the use of digital information (produced, stored and transmitted by computers) as source of evidence
More informationIntroduction 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 informationDan French Founder & CEO, Consider Solutions
Dan French Founder & CEO, Consider Solutions CONSIDER SOLUTIONS Mission Solutions for World Class Finance Footprint Financial Control & Compliance Risk Assurance Process Optimization CLIENTS CONTEXT The
More informationEvolutionary Detection of Rules for Text Categorization. Application to Spam Filtering
Advances in Intelligent Systems and Technologies Proceedings ECIT2004  Third European Conference on Intelligent Systems and Technologies Iasi, Romania, July 2123, 2004 Evolutionary Detection of Rules
More informationBusiness Intelligence and Decision Support Systems
Chapter 12 Business Intelligence and Decision Support Systems Information Technology For Management 7 th Edition Turban & Volonino Based on lecture slides by L. Beaubien, Providence College John Wiley
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 4day class is the first of the two data science courses taught by Rafal Lukawiecki. Some of the topics will be
More informationVisualization methods for patent data
Visualization methods for patent data Treparel 2013 Dr. Anton Heijs (CTO & Founder) Delft, The Netherlands Introduction Treparel can provide advanced visualizations for patent data. This document describes
More informationCLASSIFICATION AND CLUSTERING. Anveshi Charuvaka
CLASSIFICATION AND CLUSTERING Anveshi Charuvaka Learning from Data Classification Regression Clustering Anomaly Detection Contrast Set Mining Classification: Definition Given a collection of records (training
More informationClustering. 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 informationFlat Clustering KMeans Algorithm
Flat Clustering KMeans Algorithm 1. Purpose. Clustering algorithms group a set of documents into subsets or clusters. The cluster algorithms goal is to create clusters that are coherent internally, but
More informationProtein Protein Interaction Networks
Functional Pattern Mining from Genome Scale Protein Protein Interaction Networks YoungRae Cho, Ph.D. Assistant Professor Department of Computer Science Baylor University it My Definition of Bioinformatics
More information480093  TDS  SocioEnvironmental Data Science
Coordinating unit: Teaching unit: Academic year: Degree: ECTS credits: 2015 480  IS.UPC  University Research Institute for Sustainability Science and Technology 715  EIO  Department of Statistics and
More informationPrentice Hall Algebra 2 2011 Correlated to: Colorado P12 Academic Standards for High School Mathematics, Adopted 12/2009
Content Area: Mathematics Grade Level Expectations: High School Standard: Number Sense, Properties, and Operations Understand the structure and properties of our number system. At their most basic level
More information