Learning Machines: Foundations of Trainable Pattern-classifying SystemsMcGraw-Hill, 1965 - 137 sivua |
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Tulokset 1 - 3 kokonaismäärästä 16
Sivu 82
... reduced training sequence , we will generate a reduced weight - vector sequence . In Sŵ , Ŵ1 = W1 , and Ŵ is the result of k - 1 steps ; S✩ will have no repetitions and will therefore terminate at the kōth step if Ŵ + 1 is a weight ...
... reduced training sequence , we will generate a reduced weight - vector sequence . In Sŵ , Ŵ1 = W1 , and Ŵ is the result of k - 1 steps ; S✩ will have no repetitions and will therefore terminate at the kōth step if Ŵ + 1 is a weight ...
Sivu 89
... reduced training sequence S✰ and the reduced weight - vector sequences Sŵ1 , SŴR a corresponding sequence of vectors from the set Z. Let us denote this sequence of vectors from Z by the symbol Sz . Corresponding to the kth member , Ŷ ...
... reduced training sequence S✰ and the reduced weight - vector sequences Sŵ1 , SŴR a corresponding sequence of vectors from the set Z. Let us denote this sequence of vectors from Z by the symbol Sz . Corresponding to the kth member , Ŷ ...
Sivu 91
... reduced training sequence Sy then creates a reduced weight - vector sequence S✩ such that Ŷx • Ŵx ≤ 0 k ( 5.37 ) for all Ŷ in St and for all Ŵ in Sŵ . k Theorem 5.3 k Let y ' be a set of linearly contained patterns . Let S✩ be the ...
... reduced training sequence Sy then creates a reduced weight - vector sequence S✩ such that Ŷx • Ŵx ≤ 0 k ( 5.37 ) for all Ŷ in St and for all Ŵ in Sŵ . k Theorem 5.3 k Let y ' be a set of linearly contained patterns . Let S✩ be the ...
Sisältö
TRAINABLE PATTERN CLASSIFIERS | 1 |
11 | 30 |
PARAMETRIC TRAINING METHODS | 43 |
Tekijänoikeudet | |
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adjusted apply assume bank called cells changes Chapter classifier cluster column committee machine components Computer 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 terns theorem theory threshold training methods training patterns training procedure training sequence training subsets transformation values weight vectors Y₁ zero
Viitteet tähän teokseen
A Probabilistic Theory of Pattern Recognition Luc Devroye,László Györfi,Gabor Lugosi Rajoitettu esikatselu - 1997 |