Learning Machines: Foundations of Trainable Pattern-classifying SystemsMcGraw-Hill, 1965 - 137 sivua |
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Tulokset 1 - 3 kokonaismäärästä 13
Sivu 5
... described by such surfaces . * In general , * The mapping which takes all points having one or more irrational coordinates into category 1 and all other points ( i.e. , points all of whose coordinates are rational ) into category 2 is ...
... described by such surfaces . * In general , * The mapping which takes all points having one or more irrational coordinates into category 1 and all other points ( i.e. , points all of whose coordinates are rational ) into category 2 is ...
Sivu 41
... described some of the properties of various discriminant function families , we are now ready to discuss some training methods for selecting appropriate discriminant functions in the design of a pattern classifier . We shall begin by ...
... described some of the properties of various discriminant function families , we are now ready to discuss some training methods for selecting appropriate discriminant functions in the design of a pattern classifier . We shall begin by ...
Sivu 71
... described , the value of c determines how far the weight point is moved . We have distinguished three cases . In one case , c is a fixed constant so that the distance moved toward a particular pattern hyperplane is always the same ...
... described , the value of c determines how far the weight point is moved . We have distinguished three cases . In one case , c is a fixed constant so that the distance moved toward a particular pattern hyperplane is always the same ...
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 |