Learning Machines: Foundations of Trainable Pattern-classifying SystemsMcGraw-Hill, 1965 - 137 sivua |
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Tulokset 1 - 3 kokonaismäärästä 4
Sivu 21
... w d + 1 f + 1 or 1 - Response Threshold element Weights FIGURE 2.4 The threshold logic unit ( TLU ) The pattern dichotomizer with linear g ( X ) can be implemented ac- cording to the block diagram in Fig . 2.4 . Such a structure ...
... w d + 1 f + 1 or 1 - Response Threshold element Weights FIGURE 2.4 The threshold logic unit ( TLU ) The pattern dichotomizer with linear g ( X ) can be implemented ac- cording to the block diagram in Fig . 2.4 . Such a structure ...
Sivu 48
... w d Summing device X : Pattern +1 10 d + 1 Weights FIGURE 3.1 The optimum classifier for binary patterns whose components are statistically independent linear in this case . Therefore a TLU can be used as the optimum classifying machine ...
... w d Summing device X : Pattern +1 10 d + 1 Weights FIGURE 3.1 The optimum classifier for binary patterns whose components are statistically independent linear in this case . Therefore a TLU can be used as the optimum classifying machine ...
Sivu 66
... Wd , Wd + 1 . Training a TLU to dichotomize correctly the training subsets is equivalent to find- ing a set of weights such that the hyperplane separates X1 and X2 . The training methods to be described here call for iterative weight ...
... Wd , Wd + 1 . Training a TLU to dichotomize correctly the training subsets is equivalent to find- ing a set of weights such that the hyperplane separates X1 and X2 . The training methods to be described here call for iterative weight ...
Sisältö
TRAINABLE PATTERN CLASSIFIERS | 1 |
PARAMETRIC TRAINING METHODS | 43 |
SOME NONPARAMETRIC TRAINING METHODS | 65 |
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adjusted apply assume bank belonging to category called changes Chapter cluster committee components consider consists contains correction corresponding 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 discriminant functions linear machine linearly separable 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 reduced regions respect response rule sample mean selection separable shown side space Stanford step subsidiary discriminant 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 |