Learning Machines: Foundations of Trainable Pattern-classifying SystemsMcGraw-Hill, 1965 - 137 sivua |
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Tulokset 1 - 3 kokonaismäärästä 18
Sivu 72
... ( solution ) region . In this simple example , a solution occurs after five adjustments . Note that an adjustment to correct the response for one pattern may very well undo a correction made on a previous pattern . Eventually , however ...
... ( solution ) region . In this simple example , a solution occurs after five adjustments . Note that an adjustment to correct the response for one pattern may very well undo a correction made on a previous pattern . Eventually , however ...
Sivu 84
... solution vector exists . ( A similar proof can be given for arbitrary Ŵ1 . ) But , since every pattern in y occurs infinitely often in the training se- quence , termination can occur only if a solution vector is found , which proves the ...
... solution vector exists . ( A similar proof can be given for arbitrary Ŵ1 . ) But , since every pattern in y occurs infinitely often in the training se- quence , termination can occur only if a solution vector is found , which proves the ...
Sivu 86
... Solution region , W 2 W.Y = 0 2 FIGURE 5.1 A solution region W and an insulated region W as used in proof 2 of Theorem 5.1 Employing the fact that Ŵx + 1 = Ŵ1⁄2 + Ŷ and using Eq . ( 5 · 23 ) we then k obtain dk + 1 = k k −2Ŵx • Ŷx + 2W ...
... Solution region , W 2 W.Y = 0 2 FIGURE 5.1 A solution region W and an insulated region W as used in proof 2 of Theorem 5.1 Employing the fact that Ŵx + 1 = Ŵ1⁄2 + Ŷ and using Eq . ( 5 · 23 ) we then k obtain dk + 1 = k k −2Ŵx • Ŷx + 2W ...
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 |