Learning Machines: Foundations of Trainable Pattern-classifying SystemsMcGraw-Hill, 1965 - 137 sivua |
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Sivu 52
... pattern points in a two - dimensional space . Such patterns will be called bivariate normal patterns . They will ... prototype pattern for that category . As the number of sample points in a cluster increases , the coordinates of the ...
... pattern points in a two - dimensional space . Such patterns will be called bivariate normal patterns . They will ... prototype pattern for that category . As the number of sample points in a cluster increases , the coordinates of the ...
Sivu 116
... prototype " or typical pattern around which all other patterns in the category cluster . We shall say in this case that there is more than one mode per category . Suppose that there are Li prototype patterns for the ith category and ...
... prototype " or typical pattern around which all other patterns in the category cluster . We shall say in this case that there is more than one mode per category . Suppose that there are Li prototype patterns for the ith category and ...
Sivu 136
... Pattern hyperplanes , 67 negative side of , 67 positive side of , 67 Pattern point , 5 Pattern , prototype , 18 , 52 Pattern sets , linearly separable , 20 linearly contained . 82 Pattern space , 5 Pattern vector , 5 augmented , 66 Patterns ...
... Pattern hyperplanes , 67 negative side of , 67 positive side of , 67 Pattern point , 5 Pattern , prototype , 18 , 52 Pattern sets , linearly separable , 20 linearly contained . 82 Pattern space , 5 Pattern vector , 5 augmented , 66 Patterns ...
Sisältö
TRAINABLE PATTERN CLASSIFIERS | 1 |
PARAMETRIC TRAINING METHODS | 43 |
SOME NONPARAMETRIC TRAINING METHODS | 65 |
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adjusted apply assume bank belonging to category called changes Chapter cluster committee components consider consists contains correction corresponding 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 discriminant functions linear machine linearly separable 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 space Stanford step subsidiary discriminant Suppose theorem theory threshold training methods 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 |