Learning Machines: Foundations of Trainable Pattern-classifying SystemsMcGraw-Hill, 1965 - 137 sivua |
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Tulokset 1 - 3 kokonaismäärästä 14
Sivu 81
... proved quite simply as a result of Theorem 5.1 . In the modified theorem we use an absolute error- correction procedure instead of the fixed - increment error - correction pro- cedure . In the absolute error - correction procedure , the ...
... proved quite simply as a result of Theorem 5.1 . In the modified theorem we use an absolute error- correction procedure instead of the fixed - increment error - correction pro- cedure . In the absolute error - correction procedure , the ...
Sivu 92
... proving the theorem , we shall show that the sequence S✩ converges to a point P. For any fixed W in W let lim | Ŵ – W│ k → ∞ = k ( W ) ; 1 ( W ) exists since Eq . ( 5.38 ) holds for all k . We conclude that Ŵ must converge to a ...
... proving the theorem , we shall show that the sequence S✩ converges to a point P. For any fixed W in W let lim | Ŵ – W│ k → ∞ = k ( W ) ; 1 ( W ) exists since Eq . ( 5.38 ) holds for all k . We conclude that Ŵ must converge to a ...
Sivu 116
... proved in Chap- ter 5 do not apply to PWL machines . The pattern capacity of PWL ma- chines is also unknown . Even though well - developed theory is lacking , some speculations have been advanced that we shall discuss . 7.2 Training PWL ...
... proved in Chap- ter 5 do not apply to PWL machines . The pattern capacity of PWL ma- chines is also unknown . Even though well - developed theory is lacking , some speculations have been advanced that we shall discuss . 7.2 Training PWL ...
Sisältö
TRAINABLE PATTERN CLASSIFIERS | 1 |
PARAMETRIC TRAINING METHODS | 43 |
SOME NONPARAMETRIC TRAINING METHODS | 65 |
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adjusted apply assume bank called cells changes Chapter classifier cluster column committee machine components consider consists contains correction corresponding covariance decision surfaces define denote density depends described discriminant functions discussed distance distributions elements equal error-correction estimates example exist expression FIGURE fixed given implemented initial layered machine linear machine linearly separable lines majority matrix mean measurements modes negative networks nonparametric normal Note optimum origin parameters partition pattern hyperplane pattern space pattern vector pattern-classifying 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 |