Representation – Trainable Mappings
scalem | Find appropriate scaling | more routines |
bhatm | Two-class Bhattacharryya mapping | |
fisherm | Fisher mapping | |
chernoffm | Chernoff mapping | |
kernelm | Kernel mapping | |
klm | Decorrelation and Karhunen Loeve mapping (PCA) | |
klms | Scaled version of klm, useful for prewhitening | |
nlfisherm | Nonlinear Fisher mapping | |
pcam | Principal Component Analysis | |
proxm | Proximity mapping and kernel construction | |
reducm | Reduce to minimal space mapping | |
userkernel | User supplied kernel definition | |
gtm | Fit a Generative Topographic Mapping (GTM) by EM | |
som | Simple routine computing a Self-Organizing Map (SOM) | |
mapex | Support routine for training and executing a mapping with the same data. |
elements:
datasets
datafiles
cells and doubles
mappings
classifiers
mapping types.
operations:
datasets
datafiles
cells and doubles
mappings
classifiers
stacked
parallel
sequential
dyadic.
user commands:
datasets
representation
classifiers
evaluation
clustering
examples
support routines.
introductory examples:
Introduction
Scatterplots
Datasets
Datafiles
Mappings
Classifiers
Evaluation
Learning curves
Feature curves
Dimension reduction
Combining classifiers
Dissimilarities.
advanced examples.