Classifiers
MUMT611
Introduction
Preprocessing
e.g.: segmentation, FFT, MFCC
Feature extraction
e.g.: centorid, area
Feature selection
Classification
Trainig
Validation
holdout method
k-fold cross-validation
Bootstrapping
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
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
Neural networks
What is a Neural Network
(1-page)
Neural Net Overview
(Frohlich)
Kohonen demo
Neural Nets
(book by Gurney)
Neural Networks
(Stergou & Signos)
Introduction (StatSoft)
FAQ
Demo: Character recognition (Java)
Single-layer perceptron (Java)
Support Vector Machine
Support Vector Machine Tutorial
(Ridder)
Support Vector Machine Light (source code)
SVM papers
SVM applets
Hidden Markov Model
Tutorial
(Kanungo)
Links
Genetic Algorithms Links
Created: 2003.03.12
Modified: Ichiro Fujinaga