Learning Machines: Foundations of Trainable Pattern-classifying SystemsMcGraw-Hill, 1965 - 137 sivua |
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Tulokset 1 - 3 kokonaismäärästä 26
Sivu 20
... exist such that • • · 9 XR are ... , 9R for all X in Xi • • R , ji for all i = 1 , R ( 2.9 ) · " gi ( X ) > gi ( X ) ... exists such that 2. We say that a dichotomy if and g ( X ) > 0 g ( X ) < 0 for all X in X1 for all X in X2 ( 2.10 ) ...
... exist such that • • · 9 XR are ... , 9R for all X in Xi • • R , ji for all i = 1 , R ( 2.9 ) · " gi ( X ) > gi ( X ) ... exists such that 2. We say that a dichotomy if and g ( X ) > 0 g ( X ) < 0 for all X in X1 for all X in X2 ( 2.10 ) ...
Sivu 87
... exists . 5.5 A training theorem for R - category linear machines Suppose we are given R finite subsets of augmented training pattern vec- tors Y1 , Y2 , ... , YR . These subsets are linearly separable if and only if there exist R ...
... exists . 5.5 A training theorem for R - category linear machines Suppose we are given R finite subsets of augmented training pattern vec- tors Y1 , Y2 , ... , YR . These subsets are linearly separable if and only if there exist R ...
Sivu 99
... exist any training theorems for committee machines parallel to those given in Chapter 5 for machines . There does exist , however , a training procedure which has proven satisfactory in a variety of different experiments . This ...
... exist any training theorems for committee machines parallel to those given in Chapter 5 for machines . There does exist , however , a training procedure which has proven satisfactory in a variety of different experiments . This ...
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 |
Kirjaluettelon tiedot
| Otsikko | Learning Machines: Foundations of Trainable Pattern-classifying Systems McGraw-Hill series in systems science |
| Kirjoittaja | Nils J. Nilsson |
| Painos | kuvitettu |
| Kustantaja | McGraw-Hill, 1965 |
| ISBN | 0070465703, 9780070465701 |
| Pituus | 137 sivua |
| Vie sitaatti | BiBTeX EndNote RefMan |