Learning Machines: Foundations of Trainable Pattern-classifying SystemsMcGraw-Hill, 1965 - 137 sivua |
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Sivu 4
... specific to a particular application and thus would not be appropriate topics in a general treatment such as this . We shall henceforth assume that the d measurements yielding the pattern to be classified have been selected as wisely as ...
... specific to a particular application and thus would not be appropriate topics in a general treatment such as this . We shall henceforth assume that the d measurements yielding the pattern to be classified have been selected as wisely as ...
Sivu 30
... specific quadric function from among a whole family of quadric functions appear linearly in the function . There is an important class of function families whose parameters have this property . We shall call the members of these ...
... specific quadric function from among a whole family of quadric functions appear linearly in the function . There is an important class of function families whose parameters have this property . We shall call the members of these ...
Sivu 45
... specific X and for all possible values of i ( i = 1 ,. R ) . Suppose for some specific X , Lx ( i ) is a minimum for iio . That is , Lx ( io ) Lx ( i ) for i = 1 , . . . , R. We ≤ would minimize the conditional average loss if the ...
... specific X and for all possible values of i ( i = 1 ,. R ) . Suppose for some specific X , Lx ( i ) is a minimum for iio . That is , Lx ( io ) Lx ( i ) for i = 1 , . . . , R. We ≤ would minimize the conditional average loss if the ...
Sisältö
TRAINABLE PATTERN CLASSIFIERS | 1 |
PARAMETRIC TRAINING METHODS | 43 |
SOME NONPARAMETRIC TRAINING METHODS | 65 |
Tekijänoikeudet | |
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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 |