Tutorials on Topics in Statistical Pattern Recognition

1. General

   1. Glossary
   2. Overview

2. Specific Methods

   1. Distance Metrics
   2. Bayesian Methods
   3. Sequential Classifiers
   4. Discriminating Feature Methods
   5. Fuzzy
   6. K-Nearest Neighbor
   7. Linear and Higer Order Discriminant Functions
   8. Minimum Distance
   9. Multi-classifier fusion
   10. Neural Network and Perceptron Methods
   11. Markov Random Field
   12. Genetic Algorithm
   13. Nonparametric Estimation
   14. Support Vector Machine
   15. Expectation Maximization
   16. Etc.

3. Applications

   1. Face Recognition
   2. Hand-Writing Recognition
   3. Other Applications

Details of the content

1. General

   1. Glossary
      • A Statistical Learning/Pattern Recognition Glossary Thomas Minka (Microsoft)
   2. Overview
      • PATTERN RECOGNITION Robi Polikar (Rowan University)
      • Statistical Pattern Recognition  Dongil Shin (Sejong University) 
      • Statistical Pattern Recognition: A Review Anil K. Jain (Fellow, IEEE), Robert P.W. Duin, and Jianchang Mao (Senior Member, IEEE) 
      • Introduction to Statistical Learning Theory Olivier Bousquet, Stephane Boucheron, and Gabor Lugosi 
      • Statistical pattern matching: Outline Rolf Carlson (KTH) 
      • Data Clustering: A Review A.K. JAIN / Michigan State University, M.N. MURTY / Indian Institute of Science, P.J. FLYNN / The Ohio State University 
      • Introduction to statistical pattern recognition Andrew Web 

2. Specific Methods

   1. Distance Metrics
      • Asynchronous Pattern Matching - Metrics Amihood Amir
      • Metrics and Tangent Distance Department of Computer Science and Engineering, University at Buffalo
   2. Bayesian Methods
      • Bayesian Decision Theory Department of Electrical Engineering, Columbia University
      • Maximum-Likelihood EstimationDepartment of Computer Science and Engineering, University at Buffalo
      • Bayesian Parameter Estimation Department of Computer Science and Engineering, University at Buffalo
      • Bayesian Networks, Compound Decision Theory Department of Computer Science and Engineering, University at Buffalo
   3. Sequential Classifiers
      • Sequential Data and Hidden Markov Models Department of Computer Science and Engineering, University at Buffalo
   4. Discriminating Feature Methods
      • Feature Selection Based on Mutual Information: Criteria of Max-Dependency, Max-Relevance, and Min-Redundancy IEEE Transactions on Pattern Analysis and Machine Intelligence 
      • A Bayesian Discriminating Features Method for Face Detection Chengjun Liu, Member, IEEE
   5. Fuzzy
      • Genetic algorithm for optimizing fuzzy image pattern matching Janne Koljonen and Jarmo T. Alander
   6. K-Nearest Neighbor
      • K-Nearest Neighbor Algorithm Kardi Teknomo 
   7. Linear and Higer Order Discriminant Functions
      • Fisher Linear Discriminant Department of Computer Science and Engineering, University at Buffalo
   8. Minimum Distance
      • Minimum Squared Error Procedures Department of Computer Science and Engineering, University at Buffalo
   9. Multi-classifier fusion
      • Bootstrap Sampling Tutorial Kardi Teknomo 
   10. Neural Network and Perceptron Methods
       • Neural Networks IBM-ABLE framework
       • Convolutional nets: "Gradient-based Learning Applied to Document Recognition" LeCun, Bottou, Bengio, and Haffner
       • Advances in Neural Information Processing Systems, Volumes 0-13 Neural Information Processing Systems (NIPS)
       • Neural networks for maximum likelihood clustering Hazem M. Abbas, Moustafa M. Fahmy
   11. Markov Random Field
       • CRF-Matching: Conditional Random Fields for Feature-Based Scan Matching Fabio Ramos, Dieter Fox, Hugh Durrant-Whyte
       • Semi-Markov Conditional Random Fields for Information Extraction Sunita Sarawagi (Indian Institute of Technology), William W. Cohen (Center for Automated Learning & Discovery Carnegie Mellon University)
   12. Genetic Algorithm
       • Timeweaver: a Genetic Algorithm for Identifying Predictive Patterns in Sequences of Events Gary M. Weiss (Rutgers University and AT&T Labs)
       • Genetic algorithm for optimizing fuzzy image pattern matching Janne Koljonen and Jarmo T. Alander
       • Probabilistic Pattern Matching and the Evolution of Stochastic Regular Expressions BRIAN J. ROSS (Department of Computer Science, Brock University)
   13. Nonparametric Estimation
       • I: Density Estimation Department of Computer Science and Engineering, University at Buffalo
       • II: Density Estimation Department of Computer Science and Engineering, University at Buffalo
   14. Support Vector Machine
       • A Tutorial on Support Vector Machine for Pattern Recognition Christopher J.C. Burges
   15. Expectation Maximization
       • Expectation Maximization Department of Computer Science and Engineering, University at Buffalo
   16. Etc.
       • Pattern Matching Based on a Generalized Transform Ram Rajagopal

3. Applications

   1. Face Recognition
      • The Face Detection Homepage Robert Frischholz
      • Face Recognition Homepage Mislav Grgic
      • Face Animation HEUDIASYC research laboratory-CNRS / Compiegne University of Technology
      • A Bayesian Discriminating Features Method for Face Detection Chengjun Liu, Member, IEEE
   2. Hand-writing Recognition
      • Convolutional nets: "Gradient-based Learning Applied to Document Recognition" LeCun, Bottou, Bengio, and Haffner
      • The NICI stroke-based recognizer of on-line handwriting NICI (Nijmegen Institute for Cognition and Information)
   3. Other Applications
      • Neural networks application for financial markets Szczupak 
      • Music Meter Recognition (PS file) Jarno Sepannen, Tampere University of Technology
      • CRF-Matching: Conditional Random Fields for Feature-Based Scan Matching Fabio Ramos, Dieter Fox, Hugh Durrant-Whyte
      • Pattern matching for variation-source identification in manufacturing processes in the presence of unstructured noise. Li, Zhiguo Zhou, Shiyu Ding, Yu (IIE Transactions)
      • Pattern Matching Techniques and Their Applications to Computational Molecular Biology - A Review Eric C. Rouchka
      • Statistical density modification using local pattern matching Thomas C. Terwilliger
      • Pattern Matching using the Blur Hit-Miss Transform Dan S. Bloomberg and Luc Vincent
      • Improving Classification With Class-Independent Quality Measures: Q-stack in Face Verification Krzysztof Kryszczuk and Andrzej Drygajlo
      • Learning CRFs with Hierarchical Features: An Application to Go Scott Sanner (Researcher, Statistical Machine Learning Program, NICTA) 

Return to Student/Researcher Resource page

© 2008 Robert Fisher