A Matlab toolbox for pattern recognition Imported pages from 37Steps

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PRTools, elements, operations, user commands, introductory examples, advanced examples

PRTools Elements

Pattern recognition studies and exploits the road from observations to knowledge. In technical systems these are encoded in sensory data at the start of the road and  pattern classes and their relations at the end. The road itself is constituted by a set of transformations that gradually generalize the observations into knowledge.

The basic elements of PRTools are data, that travels along the road, and mappings, which are the transformations that constitute the road. On this page the elements are further defined. Other pages describe the basic operations between data and mappings (say, how traffic can use the road) and the large set of user commands, each offering the transport between two stops or just paving the way.

All operations in PRTools are between a few basic elements. These are variables of a special type. They are structures that contain next to the data various types of additional information, annotating the data. Operations between these variables using the standard Matlab operators (+ ,-, *, ...) are overloaded in  such a way that the additional information is taken into account and updated where needed.


Data in PRTools almost always refers to a set of objects (real world items or events) represented by observations. So any PRTools data item constitutes of at least one and usually a set of objects. The can be stored in four different variable types: datasets, datafiles, matrices of doubles and cell arrays.

A dataset may contain images as objects as well as features. Images are thereby unfolded to a vector and their original size is stored in the dataset for a proper handling. Other frequently used object representations are histograms, (multi-way) spectra, spectrograms and time signals.


The basic idea of a mapping is a transformation between two datasets. As a dataset stores vectors of constant size a mapping can be interpreted as a transformation between two vector spaces. A PRTools variable of the type mapping stores the name of a routine that can execute the mapping and all further information that is needed, like parameter values and names of classes and features needed to construct the proper output dataset. There are several mapping types:

Classifiers are a special type of trainable mappings. Their output space is a set of distances, confidences or class posteriors by which the best class can be selected: the class label.

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:IntroductionScatterplotsDatasets Datafiles Mappings Classifiers Evaluation Learning curves Feature curves Dimension reductionCombining classifiers Dissimilarities.
advanced examples.