Learning Machines: Foundations of Trainable Pattern-classifying Systems |
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Foundations of Trainable Pattern-classifying Systems Nils J. Nilsson. We see then that the effect of the K constraints imposed by Z is to reduce the dimensionality of the space by K. We then have Lz ( N , d ) = L ( N , d - K ) .
We recall that a TLU implements a hyperplane decision surface which divides the pattern space into two half - spaces . One of these halfspaces is Rı ; the other is R2 . The hyperplane separating these half - spaces is determined by the ...
locus of all weight points for which W · Y = 0 ( 4.2 ) a The hyperplane in weight space defined by Eq . ( 4.2 ) for a given pattern vector is called the pattern hyperplane . This hyperplane separates the space of weight points into two ...
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adjusted apply assume bank belonging to category called changes Chapter cluster committee components consider consists contains correction corresponding covariance decision surfaces define denote density depends derivation described Development discriminant functions discussed distance distribution element equal error-correction estimates example exists expression FIGURE fixed given implemented important initial layered machine linear dichotomies linear machine linearly separable matrix measurements negative networks normal Note optimum origin parameters partition pattern classifier pattern hyperplane pattern space pattern vector piecewise linear plane points positive presented probability problem proof properties proved PWL machine quadric regions respect response rule sample mean selection separable shown side solution space Stanford step Suppose theorem theory threshold training methods training procedure training sequence training subsets transformation values weight vectors zero
Viitteet tähän teokseen
A Probabilistic Theory of Pattern Recognition Luc Devroye,László Györfi,Gabor Lugosi Rajoitettu esikatselu - 1997 |