Learning Machines: Foundations of Trainable Pattern-classifying Systems |
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Sivu 76
and Farley and Clark4 and others soon afterward proposed networks of threshold
devices connected through adjustable weights ( synapses ) as model nerve nets
capable of learning . The main credit for the successful synthesis of these two ...
and Farley and Clark4 and others soon afterward proposed networks of threshold
devices connected through adjustable weights ( synapses ) as model nerve nets
capable of learning . The main credit for the successful synthesis of these two ...
Sivu 95
CHAPTER LAYERED MACHINES 6 : 1 Layered networks of TLUS sample ,
expressions a Networks of interconnected TLUs have often been proposed as
patternclassifying machines . In these networks the binary responses of some
TLUs are ...
CHAPTER LAYERED MACHINES 6 : 1 Layered networks of TLUS sample ,
expressions a Networks of interconnected TLUs have often been proposed as
patternclassifying machines . In these networks the binary responses of some
TLUs are ...
Sivu 135
... 62 , 63 Kaylor , 113 , 114 Keehn , 62 , 63 Kesler , 77 , 93 Koford , 40 , 41
Layered machines , 95 discriminant functions for , 109 Layered networks of TLUs
, 95 Learning matrix , 40 Networks of TLUs , layered , 95 Neuron model , INDEX
135.
... 62 , 63 Kaylor , 113 , 114 Keehn , 62 , 63 Kesler , 77 , 93 Koford , 40 , 41
Layered machines , 95 discriminant functions for , 109 Layered networks of TLUs
, 95 Learning matrix , 40 Networks of TLUs , layered , 95 Neuron model , INDEX
135.
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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 |