Classifiers – Various
| nbayesc | Bayes classifier for given normal densities | more routines | 
| mogc | Mixture of gaussians classification | |
| knnc | k-nearest neighbour classifier (find k, build classifier) | |
| statsknnc | Statistical toolbox nearest neighbor classifier | |
| parzenc | Parzen classifier | |
| parzendc | Parzen density based classifier | |
| adaboostc | ADABoost classifier | |
| rfishercc | Random Fisher combining classifier | |
| treec | Construct binary decision tree classifier | |
| dtc | Decision tree classifier, rewritten, also for nominal features | |
| statsdtc | Statistical toolbox decision tree | |
| randomforestc | Breiman’s random forest classifier | |
| naivebc | Naive Bayes classifier | |
| statsnbc | Statistical toolbox naive Bayes classifier | |
| fdsc | Feature based dissimilarity space classifier | 
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.



