Learning Machines: Foundations of Trainable Pattern-classifying SystemsMcGraw-Hill, 1965 - 137 sivua |
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... probability - density function In the example of Sec . 3.5 , we assumed that the pattern components were statistically independent , binary , random variables . Such an assumption permitted a straightforward calculation of the ...
... probability - density function In the example of Sec . 3.5 , we assumed that the pattern components were statistically independent , binary , random variables . Such an assumption permitted a straightforward calculation of the ...
Sivu 118
... probability of error . This is so because the underlying probability distributions may be sufficiently overlapping that the optimum decision surfaces do not perfectly separate the training subsets . Consider , for example , the ...
... probability of error . This is so because the underlying probability distributions may be sufficiently overlapping that the optimum decision surfaces do not perfectly separate the training subsets . Consider , for example , the ...
Sivu 120
... probability distributions are normal.3 There does exist a simple nonparametric rule , however , whose use implies only that the probability - density functions exist and that they are continuous . We shall call this rule the Fix and ...
... probability distributions are normal.3 There does exist a simple nonparametric rule , however , whose use implies only that the probability - density functions exist and that they are continuous . We shall call this rule the Fix and ...
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 |