Learning Machines: Foundations of Trainable Pattern-classifying Systems |
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Sivu 70
For one of them , called the fixed - increment rule , c is taken to be any fixed
number greater than zero . When c is equal to one , for example , each weight is
altered by the addition ( or subtraction ) of the corresponding pattern component .
For one of them , called the fixed - increment rule , c is taken to be any fixed
number greater than zero . When c is equal to one , for example , each weight is
altered by the addition ( or subtraction ) of the corresponding pattern component .
Sivu 77
The fixed - increment and absolute correction rules were first proposed by
Rosenblatt , 13 although Widrow and Hoff 8 introduced a similar rule at
substantially the same time . Ridgway10 later suggested a modification of the
Widrow - Hoff rule ...
The fixed - increment and absolute correction rules were first proposed by
Rosenblatt , 13 although Widrow and Hoff 8 introduced a similar rule at
substantially the same time . Ridgway10 later suggested a modification of the
Widrow - Hoff rule ...
Sivu 133
INDEX Abramson , 61 , 62 , 63 Absolute correction rule , 70 , 81 ADALINES , 77
Adaptive decision networks , 2 Adaptive sample set construction , 125 Adjusted
training set , 81 Adjustment , of discriminant functions , of weight vectors , 67 , 69
...
INDEX Abramson , 61 , 62 , 63 Absolute correction rule , 70 , 81 ADALINES , 77
Adaptive decision networks , 2 Adaptive sample set construction , 125 Adjusted
training set , 81 Adjustment , of discriminant functions , of weight vectors , 67 , 69
...
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Sisältö
Preface vii | 1 |
SOME NONPARAMETRIC TRAINING METHODS | 65 |
TRAINING THEOREMS | 79 |
Tekijänoikeudet | |
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adjusted apply assume bank belonging to category called changes Chapter cluster committee components consider consists contains correction corresponding covariance decision surfaces define denote density depends derivation described Development discriminant functions discussed distance distribution element equal error-correction estimates example exists expression FIGURE fixed given implemented important initial layered machine linear dichotomies linear machine linearly separable matrix measurements negative networks normal Note optimum origin parameters partition pattern classifier pattern hyperplane pattern space pattern vector piecewise linear plane points positive presented probability problem proof properties proved PWL machine quadric reduced regions respect response rule sample mean selection separable shown side solution space Stanford step Suppose theorem theory threshold training methods training procedure training sequence training subsets transformation values weight vectors zero
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 |
| Pituus | 137 sivua |
| Vie sitaatti | BiBTeX EndNote RefMan |