Learning Machines: Foundations of Trainable Pattern-classifying Systems |
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Sivu 85
5 : 4 Proof 2 The following proof of Theorem 5 . 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 .
5 : 4 Proof 2 The following proof of Theorem 5 . 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 .
Sivu 89
Foundations of Trainable Pattern-classifying Systems Nils J. Nilsson. Proof The
proof of Theorem 5 . 2 is accomplished by reformulating the R - category problem
as a dichotomy problem in a higher - dimensional space and then applying ...
Foundations of Trainable Pattern-classifying Systems Nils J. Nilsson. Proof The
proof of Theorem 5 . 2 is accomplished by reformulating the R - category problem
as a dichotomy problem in a higher - dimensional space and then applying ...
Sivu 108
Proof We have P TLUs , each of which implements a hyperplane in the pattern
space . In this proof it will be convenient for the TLUs to have 0 , 1 responses
rather than – 1 , 1 responses . Since exactly P + 1 cells are occupied by patterns
...
Proof We have P TLUs , each of which implements a hyperplane in the pattern
space . In this proof it will be convenient for the TLUs to have 0 , 1 responses
rather than – 1 , 1 responses . Since exactly P + 1 cells are occupied by patterns
...
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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 |