Learning Machines: Foundations of Trainable Pattern-classifying SystemsMcGraw-Hill, 1965 - 137 sivua |
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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 x2 R3 R2 2 OR , R , - The point ( 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 x2 R3 R2 2 OR , R , - The point ( 5 ...
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 |
PARAMETRIC TRAINING METHODS | 43 |
SOME NONPARAMETRIC TRAINING METHODS | 65 |
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adjusted apply assume bank belonging to category called changes Chapter cluster committee components consider consists contains correction corresponding decision surfaces define denote density depends derivation described Development discriminant functions discussed distance distribution element equal error-correction estimates example exists expression FIGURE fixed given implemented important initial layered machine linear dichotomies linear discriminant functions linear machine linearly separable measurements negative networks 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 space Stanford step subsidiary discriminant Suppose theorem theory threshold training methods 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 |