Learning from data is the key issue of pattern recognition. So the trainable mappings are the core of PRTools. They have two states: untrained and trained. An untrained mapping may have a number of parameter to be supplied by the user. Examples are the number of neighbors in the kNN classifier knnc, regularization parameters in density estimators like
qdc and the trade-off parameter in the support vector classifier
svc and the kernel.
Training is done by supplying a dataset to the untrained mapping. For classifiers the dataset should be labeled. Mappings like PC-analysis (
pcam) that don’t need labels will also operate on doubles. The result of an untrained mapping is a trained mapping which is ready to be used for mapping new data to a space or a set of labels for which the routine is optimized.
The following rules apply if a dataset
A is processed by a sequential combination of a fixed mapping
F, a trained mapping
T and an untrained mapping
||Training an untrained mapping results in a trained mapping|
||The data is mapped by
||By combining a fixed mapping with an untrained mapping before training is started, PRTools is able to combine the fixed mapping with the trained result. Herewith new data can be directly applied to
||Combining untrained mappings results in a new untrained mapping.|
||Training a set of two untrained mappings like this just trains the first.|
||Training a combined set of untrained mappings trains both, in a proper way and combines the combined trained mapping such that it can be directly applied to new data from the same source as
||Training a stacked combiner, combined by the fixed combiner
||Training a stacked combiner, combined by the yet untrained combiner
||Training a parallel combiner, combined by the fixed combiner
||Training a parallel combiner, combined by the yet untrained combiner
An example is:
A1 = gendatb; % training set
A2 = gendatb; % independent test set
T1 = A1*(pcam(,1)*fisherc);
T2 = A1*pcam(,1)*(A1*(A1*pcam(,1))*fisherc);
The results for
T2 are the same, as PRTools implicitly computes
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.