Classifiers
MUMT621
Introduction
Preprocessing
e.g.: segmentation, windowing, FFT, MFCC (mel-frequency cepstrum coefficients)
Feature extraction
e.g.: centroid, area, mean
Feature selection
Classification
Training: ground-truth
Validation
holdout method
k-fold cross-validation, leave-one-out
Bootstrapping (resampling with replacement) (bagging)
Classifiers (supervised)
Bayes classifier
Support Vector Machines
Boosting algorithms
Hidden Markov models
Non-parametric density estimation (distribution-free)
k-nearest neighbour (non-greedy, lazy)
Neural networks (greedy)s
Clustering (unsupervised)
Hierachical methods
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
Created: 2003.03.12
Modified: Ichiro Fujinaga