Learning Machines: Foundations of Trainable Pattern-classifying SystemsMcGraw-Hill, 1965 - 137 sivua |
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Tulokset 1 - 3 kokonaismäärästä 34
Sivu 75
... procedure for R > 2 A linear machine for classifying patterns belonging to more than two categories was defined in Chapter 2. It consists of R linear discriminators and a maximum selector ( Fig . 2 · 1 ) ... training procedure for R > 2,
... procedure for R > 2 A linear machine for classifying patterns belonging to more than two categories was defined in Chapter 2. It consists of R linear discriminators and a maximum selector ( Fig . 2 · 1 ) ... training procedure for R > 2,
Sivu 119
... training methods . Suppose that we decided to use an error - correction training procedure to train a single TLU . Even though a TLU is capable of implementing the optimum decision surface , an error - correction procedure could never ...
... training methods . Suppose that we decided to use an error - correction training procedure to train a single TLU . Even though a TLU is capable of implementing the optimum decision surface , an error - correction procedure could never ...
Sivu 122
... training set . In the next section we shall present a candidate training method . 7.6 Mode - seeking and related training methods for PWL machines " To apply the closest - mode decision method , we need a training procedure to locate ...
... training set . In the next section we shall present a candidate training method . 7.6 Mode - seeking and related training methods for PWL machines " To apply the closest - mode decision method , we need a training procedure to locate ...
Sisältö
Preface vii | 11 |
PARAMETRIC TRAINING METHODS | 43 |
SOME NONPARAMETRIC TRAINING METHODS | 65 |
Tekijänoikeudet | |
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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 important 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 |