Learning Machines: Foundations of Trainable Pattern-classifying Systems |
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We shall not consider in this book any mappings requiring an infinite number of decision surfaces or any mappings that cannot be described by such surfaces . * In general , * The mapping which takes all points having one or more ...
We shall not consider in this book any mappings requiring an infinite number of decision surfaces or any mappings that cannot be described by such surfaces . * In general , * The mapping which takes all points having one or more ...
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2.7 Piecewise linear discriminant functions а As a special case of discriminant functions which we shall call piecewise linear , we shall first consider those of a minimum - distance classifier with respect to point sets .
2.7 Piecewise linear discriminant functions а As a special case of discriminant functions which we shall call piecewise linear , we shall first consider those of a minimum - distance classifier with respect to point sets .
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Consider the three pattern hyperplanes ( lines ) in Fig . 6.3 for the case D = d + 1 = 2. The arrows attached to the lines point toward the positive sides of the lines . These lines divide the weight space into six regions ...
Consider the three pattern hyperplanes ( lines ) in Fig . 6.3 for the case D = d + 1 = 2. The arrows attached to the lines point toward the positive sides of the lines . These lines divide the weight space into six regions ...
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Sisältö
I | 1 |
SOME NONPARAMETRIC TRAINING METHODS | 65 |
APPENDIX | 127 |
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adjusted apply assume bank belonging to category called changes Chapter cluster committee components consider consists contains correction corresponding covariance decision surfaces define denote density depends derivation described discriminant functions discussed distance distribution element equal error-correction estimates example exists expression FIGURE fixed given implemented important initial layered machine linear dichotomies linear machine linearly separable matrix measurements negative 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 solution space specific Stanford step Suppose theorem theory threshold training methods training patterns 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 |