Learning Machines: Foundations of Trainable Pattern-classifying SystemsMcGraw-Hill, 1965 - 137 sivua |
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And McGinnis' Theorem of Derivative Equations in an Absolute Proof of Fermat's Theorem ; Reduction of the General Equation of the Fifth Degree to an Equation of the Fourth Degree ; and Supplementary Theorems Michael Angelo McGinnis.
And McGinnis' Theorem of Derivative Equations in an Absolute Proof of Fermat's Theorem ; Reduction of the General Equation of the Fifth Degree to an Equation of the Fourth Degree ; and Supplementary Theorems Michael Angelo McGinnis.
Sivu 50
... Theorem 56 is an example of the sort of result that is required if we are to allow that a relation may map the Gs into the Fs, yet relate non-Gs to objects as well (and possibly to Fs). The proof of Theorem 66 from Theorems 63 and 56 ...
... Theorem 56 is an example of the sort of result that is required if we are to allow that a relation may map the Gs into the Fs, yet relate non-Gs to objects as well (and possibly to Fs). The proof of Theorem 66 from Theorems 63 and 56 ...
Sivu 127
... theorem for func- tions in two variables . Further , we study some functional equations arising from the mean value theorem for functions in two variables . The functional equations studied in this chapter are related to the ...
... theorem for func- tions in two variables . Further , we study some functional equations arising from the mean value theorem for functions in two variables . The functional equations studied in this chapter are related to the ...
Sisältö
TRAINABLE PATTERN CLASSIFIERS | 1 |
PARAMETRIC TRAINING METHODS | 43 |
SOME NONPARAMETRIC TRAINING METHODS | 65 |
Tekijänoikeudet | |
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adjusted apply assume bank called cells changes Chapter classifier cluster column committee machine components consider consists contains correction corresponding covariance decision surfaces define denote density depends described discriminant functions discussed distance distributions elements equal error-correction estimates example exist expression FIGURE fixed given implemented initial layered machine linear machine linearly separable lines majority matrix mean measurements modes negative networks nonparametric normal Note optimum origin parameters partition pattern hyperplane pattern space pattern vector pattern-classifying piecewise linear plane points positive presented probability problem properties PWL machine quadric regions respect response rule selection separable sequence side solution space Stanford step subsidiary discriminant Suppose theorem theory threshold training methods training patterns training procedure training sequence training subsets transformation values weight vectors X1 and X2 Y₁ zero
Viitteet tähän teokseen
A Probabilistic Theory of Pattern Recognition Luc Devroye,László Györfi,Gabor Lugosi Rajoitettu esikatselu - 1997 |