Representation – Trainable Mappings
|scalem||Find appropriate scaling||more routines|
|bhatm||Two-class Bhattacharryya 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.|
cells and doubles
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.