Combining Classifiers

averagec Combining linear classifiers by averaging coefficients  more routines
baggingc Bootstrapping and aggregation of classifiers
dcsc Dynamic Classifier Selecting Combiner
modselc Model Selection Combiner (Static selection)
rsscc Random subspace combining classifier
votec Voting classifier combiner
wvotec Weighted voting classifier combiner
maxc Maximum classifier combiner
minc Minimum classifier combiner
meanc Mean classifier combiner
medianc Median classifier combiner
mlrc Multi-response linear regression combiner
naivebcc Naive Bayes classifier combiner
perc Percentile combiner
prodc Product classifier combiner
traincc Train combining classifier
rejectc Creates reject version of existing classifier
parallel Parallel combining of classifiers
stacked Stacked combining of classifiers
sequential  Sequential combining of classifiers

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.