Learning Machines: Foundations of Trainable Pattern-classifying SystemsMcGraw-Hill, 1965 - 137 sivua |
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Tulokset 1 - 3 kokonaismäärästä 18
Sivu 34
... illustrate , let H ; be a hyperplane which partitions X ' and suppose that H ; can be made to pass through Xy without ... illustrated in Fig . 2.10 for the case d = 2 . recursion relation L ( N , d ) = L 34 SOME IMPORTANT DISCRIMINANT ...
... illustrate , let H ; be a hyperplane which partitions X ' and suppose that H ; can be made to pass through Xy without ... illustrated in Fig . 2.10 for the case d = 2 . recursion relation L ( N , d ) = L 34 SOME IMPORTANT DISCRIMINANT ...
Sivu 72
... illustrated in Fig . 4-2 . There are four patterns represented by pattern hyperplanes in weight space . The small arrows attached to these planes in this case indicate the side on which a TLU weight vector will give the desired response ...
... illustrated in Fig . 4-2 . There are four patterns represented by pattern hyperplanes in weight space . The small arrows attached to these planes in this case indicate the side on which a TLU weight vector will give the desired response ...
Sivu 101
... illustrate this training procedure for an example in which we have three augmented patterns of two dimensions . 6.4 An example The training procedure described above can be illustrated quite clearly by a two - dimensional example . The ...
... illustrate this training procedure for an example in which we have three augmented patterns of two dimensions . 6.4 An example The training procedure described above can be illustrated quite clearly by a two - dimensional example . The ...
Sisältö
TRAINABLE PATTERN CLASSIFIERS | 1 |
SOME NONPARAMETRIC TRAINING METHODS | 65 |
TRAINING THEOREMS | 79 |
Tekijänoikeudet | |
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adjusted apply assume bank called cells changes Chapter cluster column committee machine components consider consists contains correction corresponding covariance decision surfaces define denote density depends described dichotomies discriminant functions discussed distance distributions elements equal error-correction estimates example exist expression FIGURE fixed given implemented important initial layered machine linear machine linearly separable lines majority matrix mean measurements modes negative networks nonparametric normal Note optimum origin parameters partition pattern classifier pattern hyperplane pattern space pattern vector 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 |