Learning Machines: Foundations of Trainable Pattern-classifying Systems |
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This hyperplane separates the space of weight points into two classes : Those which for the pattern Y produce a TLU response of one are on one side of the hyperplane , called the positive side , and those which produce a TLU response of ...
This hyperplane separates the space of weight points into two classes : Those which for the pattern Y produce a TLU response of one are on one side of the hyperplane , called the positive side , and those which produce a TLU response of ...
Sivu 69
That is , W is either on the negative side of or on the pattern hyperplane corresponding to Y. This error can be rectified by moving W to the positive side of the pattern hyperplane . The most direct path to the other side is along a ...
That is , W is either on the negative side of or on the pattern hyperplane corresponding to Y. This error can be rectified by moving W to the positive side of the pattern hyperplane . The most direct path to the other side is along a ...
Sivu 71
For each pattern hyperplane , one inquires whether or not the weight point is on the desired side . If it is on the desired side , the next pattern hyperplane in the training set is examined . If it is not , the weight point is moved ...
For each pattern hyperplane , one inquires whether or not the weight point is on the desired side . If it is on the desired side , the next pattern hyperplane in the training set is examined . If it is not , the weight point is moved ...
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Sisältö
I | 1 |
SOME NONPARAMETRIC TRAINING METHODS | 65 |
APPENDIX | 127 |
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 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 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 specific Stanford step Suppose theorem theory threshold training methods training patterns training procedure training sequence training subsets transformation values weight vectors zero
Viitteet tähän teokseen
A Probabilistic Theory of Pattern Recognition Luc Devroye,László Györfi,Gabor Lugosi Rajoitettu esikatselu - 1997 |