Classifiers

MUMT611

Introduction

• Preprocessing
  • e.g.: segmentation, FFT, MFCC
• Feature extraction
  • e.g.: centroid, area
• Feature selection
• Classification
  • Training: ground-truth
  • Validation
    • holdout method
    • k-fold cross-validation
    • Bootstrapping (resampling with replacement) (bagging)

Classifiers (supervised)

• Bayes classifier
• Support Vector Machines
• Hidden Markov models
• Non-parametric density estimation (distribution-free)
  • k-nearest neighbour
  • Neural networks

Clustering (unsupervised)

• Hierachical methods (an example)
• k-means (demo)
  • Gaussian mixture
• Self Organizing Maps (demo)

Resources

• General
  • Pattern Recognition (Toussaint)
  • Weka (readme)
  • Michie, D., Spiegelhalter, D.J. and Taylor, C.C. 1994. Machine Learning, Neural and Statistical Classification. (online book)
  • Statistical Data Mining Tutorials (Andrew Moore)
  • Various links
  • Classifier showdown
• Bayes classifiers
  • Naive Bayes Classifier
• Neural networks
  • What is a Neural Network (1-page)
  • Neural Net Overview (Frohlich) Kohonen demo
  • Neural Networks (Stergou & Signos)
  • Introduction (StatSoft)
  • FAQ
  • Single-layer perceptron (Java)
• Support Vector Machine
  • Support Vector Machine Tutorial (Ridder)
  • Support Vector Machine Light (source code)
  • SVM applets
• Hidden Markov Model
  • Tutorial (Kanungo)
  • Links
• Genetic Algorithms Links
• MUMT621 (2009): Classifiers