Learning Machines: Foundations of Trainable Pattern-classifying Systems |
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Sivu 72
By convergent we mean that when the pattern training subsets are linearly
separable , the sequence of TLU weight vectors produced by the training
procedure converges toward a solution weight vector . The fixed - increment and
absolute ...
By convergent we mean that when the pattern training subsets are linearly
separable , the sequence of TLU weight vectors produced by the training
procedure converges toward a solution weight vector . The fixed - increment and
absolute ...
Sivu 84
... number of changes made in the weight vector . Clearly , the bounds given by
inequalities ( 5 : 14 ) and ( 5•20 ) conflict for sufficiently large values of k .
Certainly k can be no larger than kim , which is a solution to the equation kmM =
W12 2 .
... number of changes made in the weight vector . Clearly , the bounds given by
inequalities ( 5 : 14 ) and ( 5•20 ) conflict for sufficiently large values of k .
Certainly k can be no larger than kim , which is a solution to the equation kmM =
W12 2 .
Sivu 85
1 results from a simple geometric argument revealing that it is impossible to apply
the fixed - increment error - correction procedure and remain forever outside the
region of solution vectors . Let W be the solution region of weight vectors ...
1 results from a simple geometric argument revealing that it is impossible to apply
the fixed - increment error - correction procedure and remain forever outside the
region of solution vectors . Let W be the solution region of weight vectors ...
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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 |