Learning Machines: Foundations of Trainable Pattern-classifying SystemsMcGraw-Hill, 1965 - 137 sivua |
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Sivu 72
... correction rule is convergent . By convergent we mean that when the pattern training subsets are linearly separable , the sequence of TLU weight vectors produced by the training procedure con- verges toward a ... error-correction training,
... correction rule is convergent . By convergent we mean that when the pattern training subsets are linearly separable , the sequence of TLU weight vectors produced by the training procedure con- verges toward a ... error-correction training,
Sivu 118
... error- correction procedure to a structure containing more than one subsidiary discriminant function per bank . The conditions ( if any ) under which this procedure terminates in a solution , when a ... error-correction training methods,
... error- correction procedure to a structure containing more than one subsidiary discriminant function per bank . The conditions ( if any ) under which this procedure terminates in a solution , when a ... error-correction training methods,
Sivu 119
... error - correction training procedure to train a single TLU . Even though a TLU is capable of implementing the optimum decision surface , an error - correction procedure could never stabilize at the optimum surface since inevitable errors ...
... error - correction training procedure to train a single TLU . Even though a TLU is capable of implementing the optimum decision surface , an error - correction procedure could never stabilize at the optimum surface since inevitable errors ...
Sisältö
TRAINABLE PATTERN CLASSIFIERS | 1 |
PARAMETRIC TRAINING METHODS | 43 |
SOME NONPARAMETRIC TRAINING METHODS | 65 |
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adjusted apply assume bank called cells changes Chapter classifier cluster column committee machine components consider consists contains correction corresponding covariance decision surfaces define denote density depends described discriminant functions discussed distance distributions elements equal error-correction estimates example exist expression FIGURE fixed given implemented initial layered machine linear machine linearly separable lines majority matrix mean measurements modes negative networks nonparametric normal Note optimum origin parameters partition pattern hyperplane pattern space pattern vector pattern-classifying piecewise linear plane points positive presented probability problem properties PWL machine quadric regions respect response rule selection separable sequence side solution space Stanford step subsidiary discriminant Suppose theorem theory threshold training methods training patterns training procedure training sequence training subsets transformation values weight vectors X1 and X2 Y₁ zero
Viitteet tähän teokseen
A Probabilistic Theory of Pattern Recognition Luc Devroye,László Györfi,Gabor Lugosi Rajoitettu esikatselu - 1997 |