Learning Machines: Foundations of Trainable Pattern-classifying SystemsMcGraw-Hill, 1965 - 137 sivua |
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Tulokset 1 - 3 kokonaismäärästä 18
Sivu 69
... presented to the trainable TLU one at a time for trial . The trial consists of comparing the actual response of the ... presented by cycling through the training set , over and over , or the patterns may be presented in some random order ...
... presented to the trainable TLU one at a time for trial . The trial consists of comparing the actual response of the ... presented by cycling through the training set , over and over , or the patterns may be presented in some random order ...
Sivu 75
... presented one at a time in any sequence . Arbitrary initial weight vectors are selected for the machine , and adjustments of these are made whenever the machine responds incor- rectly to any pattern . Suppose that a pattern Y belonging ...
... presented one at a time in any sequence . Arbitrary initial weight vectors are selected for the machine , and adjustments of these are made whenever the machine responds incor- rectly to any pattern . Suppose that a pattern Y belonging ...
Sivu 125
... presented in this chapter originated with the author , although his opinions were influenced by many . We shall try to give a short account of these influences here . The disadvantage of the error- correction methods , discussed in Sec ...
... presented in this chapter originated with the author , although his opinions were influenced by many . We shall try to give a short account of these influences here . The disadvantage of the error- correction methods , discussed in Sec ...
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 |