Learning Machines: Foundations of Trainable Pattern-classifying SystemsMcGraw-Hill, 1965 - 137 sivua |
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Tulokset 1 - 3 kokonaismäärästä 52
Sivu 5
... pattern can be represented by a point in a d - dimensional Euclidean space Ed called the pattern space . The ... points of Ed into the category numbers , 1 , . . . , R. Let the symbol R ; denote the set of R3 R 2 OR , R - The point ( 5 , -3 ) ...
... pattern can be represented by a point in a d - dimensional Euclidean space Ed called the pattern space . The ... points of Ed into the category numbers , 1 , . . . , R. Let the symbol R ; denote the set of R3 R 2 OR , R - The point ( 5 , -3 ) ...
Sivu 9
... pattern classifier is eventually to achieve must be achieved largely by an adjustment process , which has become ... points in category 1 tend to cluster close to some central cluster point X1 , and that the pattern points in category 2 ...
... pattern classifier is eventually to achieve must be achieved largely by an adjustment process , which has become ... points in category 1 tend to cluster close to some central cluster point X1 , and that the pattern points in category 2 ...
Sivu 32
... patterns that its members could effect . We shall show that if the positions of the N pattern points satisfy some quite mild conditions , the number of dichotomies that can be implemented by a function will depend only on the number of ...
... patterns that its members could effect . We shall show that if the positions of the N pattern points satisfy some quite mild conditions , the number of dichotomies that can be implemented by a function will depend only on the number of ...
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 |