Classifiers
MUMT611
Introduction
Preprocessing
e.g.: segmentation, FFT, MFCC
Feature extraction
e.g.: centroid, area
Feature selection
Classification
Training
Validation
holdout method
k-fold cross-validation
Bootstrapping (resampling with replacement)
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
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
Created: 2003.03.12
Modified: Ichiro Fujinaga