An Introduction to Multiclass Pattern Recognition in Unstructured Situations.
Interim scientific rept. no. 12,
MICHIGAN STATE UNIV EAST LANSING DIV OF ENGINEERING RESEARCH
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The M-class pattern recognition problem is to construct a set of discriminant functions hwhich partition a feature space into M regions, one region per pattern class. Each point in the feature space is a potential pattern and each pattern represents an object. Almost nothing is assumed about the origins of the patterns. Distributions are not associated with the pattern classes. A set of training patterns is to be generalized into a set of discriminant functions which classify the potential patterns. The fundamental algorithms developed here concern the situation where the origin of each training pattern is known. An extension to the unsupervised case is also given. Several new multi-class decision-making algorithms are proposed. An entirely new class of algorithms is obtained by translating the pattern recognition problem into the problem of minimizing a function of several variables and selecting suitable functions. This general formulation includes most known algorithms as special cases. The class of algorithms includes all procedures which approximate discriminant functions by linear combinations of basis functions. Several sucessful two-class algorithms are extended to the M-class problem. Author
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