Method and arrangement for pattern recognition on the basis of statistics

An arrangement for pattern recognition on the basis of statistics, which, for an object to be recognized, estimates an association of each target class of a class set with a numerical value on the basis of a complete ensemble of two- or multi-class classifiers, the numerical value resulting from cascaded application of polynomial classifiers, comprising:

• means for statistically-optimized selection of the two- or multi-class classifiers whose estimates contribute the most, with high separation relevance, to minimizing a scalar measure calculated over an estimation-vector spectrum, from a complete ensemble of all two- or multi-class classifiers based on their estimation-vector spectrum over a learning sample in which all class patterns to be recognized are represented to a sufficient extent;
• means for generating polynomial-expanded feature vectors that represent the order of the system of selected two- or multi-class classifiers, said polynomial-expanded feature vectors being formed over an expanded learning sample, said expanding carried out by means of polynomial linking;
• and
• means for calculating an assessment classifier that uses the feature vectors of the system of selected two- or multi-class classifiers to calculate an estimation vector that, for each target class, contains a numerical value as an approximated conclusion probability regarding the association of a classified pattern with said class of patterns.

A method of pattern recognition for estimating input characters using probabilities of their memberships in n character classes, comprising the steps of:

• performing an optimal selection procedure, the optimal selection procedure determining from a given system of N statistically non-independent classifiers a set of K classifiers that contribute the most to minimizing a scalar classification measure for all comparable subsystems of fixed dimension K, where K<N;
• adjusting K such that a linear K-dimensional output vector of the subsystem can be polynomially extended to at least quadratic terms below a predetermined magnitude of terms;
• and
• constructing an optimal assessment classifier that uses the polynomially-extended output vector of the optimal subsystem as its input and maps it to a final probability vector for the n classes as its output.