Learning Machines: Foundations of Trainable Pattern-classifying SystemsMcGraw-Hill, 1965 - 137 sivua |
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Tulokset 1 - 3 kokonaismäärästä 18
Sivu 69
... presented to the trainable TLU one at a time for trial . The trial consists of comparing the actual response of the ... presented by cycling through the training set , over and over , or the patterns may be presented in some random order ...
... presented to the trainable TLU one at a time for trial . The trial consists of comparing the actual response of the ... presented by cycling through the training set , over and over , or the patterns may be presented in some random order ...
Sivu 75
... presented one at a time in any sequence . Arbitrary initial weight vectors are selected for the machine , and adjustments of these are made whenever the machine responds incor- rectly to any pattern . Suppose that a pattern Y belonging ...
... presented one at a time in any sequence . Arbitrary initial weight vectors are selected for the machine , and adjustments of these are made whenever the machine responds incor- rectly to any pattern . Suppose that a pattern Y belonging ...
Sivu 123
... presented because it illustrates several that have been proposed for mode seeking . No rigorous theo- retical treatment has been advanced to support it , and only limited em- pirical evidence has been collected to justify its use , but ...
... presented because it illustrates several that have been proposed for mode seeking . No rigorous theo- retical treatment has been advanced to support it , and only limited em- pirical evidence has been collected to justify its use , but ...
Sisältö
TRAINABLE PATTERN CLASSIFIERS | 1 |
PARAMETRIC TRAINING METHODS | 43 |
SOME NONPARAMETRIC TRAINING METHODS | 65 |
Tekijänoikeudet | |
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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 |