Learning Machines: Foundations of Trainable Pattern-classifying SystemsMcGraw-Hill, 1965 - 137 sivua |
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... w d Threshold element xd Pattern w d + 1 +1 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 Threshold element xd Pattern w d + 1 +1 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 ...
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... wd +1 ZOV Gun +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 . * The block diagram of ...
... wd +1 ZOV Gun +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 . * The block diagram of ...
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... Wd + 1 . This set of weights can be represented by a point in a ( d + 1 ) -dimensional weight space . The rectangular coordinates of the point are given by the weight values . The ( d + 1 ) -dimensional vector W with components w1 , W2 ...
... Wd + 1 . This set of weights can be represented by a point in a ( d + 1 ) -dimensional weight space . The rectangular coordinates of the point are given by the weight values . The ( d + 1 ) -dimensional vector W with components w1 , W2 ...
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 step subsidiary discriminant Suppose terns 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 |