Learning Machines: Foundations of Trainable Pattern-classifying Systems |
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The present work deals specifically with the theory of a subclass of learning
machines , those which can be trained to recognize patterns . Some well - known
examples of trainable pattern - classifying systems are the PERCEPTRON and
the ...
The present work deals specifically with the theory of a subclass of learning
machines , those which can be trained to recognize patterns . Some well - known
examples of trainable pattern - classifying systems are the PERCEPTRON and
the ...
Sivu 77
Tests on a Cell Assembly Theory of the Action of the Brain Using a Large Digital
Computer , Trans . IRE on Info . Theory , vol . IT - 2 , no . 3 , pp . 80 – 93 ,
September , 1956 . 4 Farley , B . , and W . Clark : Simulation of Self - organizing
Systems ...
Tests on a Cell Assembly Theory of the Action of the Brain Using a Large Digital
Computer , Trans . IRE on Info . Theory , vol . IT - 2 , no . 3 , pp . 80 – 93 ,
September , 1956 . 4 Farley , B . , and W . Clark : Simulation of Self - organizing
Systems ...
Sivu 126
3 Sebestyen , G . : “ Decision - making Processes in Pattern Recognition , ” The
Macmillan Company , New York , 1962 . - Pattern Recognition by an Adaptive
Process of Sample Set Construction , Trans . IRE on Info . Theory , vol . 178 , no .
3 Sebestyen , G . : “ Decision - making Processes in Pattern Recognition , ” The
Macmillan Company , New York , 1962 . - Pattern Recognition by an Adaptive
Process of Sample Set Construction , Trans . IRE on Info . Theory , vol . 178 , no .
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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