Learning Machines: Foundations of Trainable Pattern-classifying SystemsMcGraw-Hill, 1965 - 137 sivua |
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Sivu 83
... W ; < 0 , we have from Eq . ( 5.8 ) Ŵk + 1 = 1 Ŵ1 + Ŷ1 + Ŷ1⁄2 + + Ŷ 2 ( 5.9 ) We shall prove the theorem for the ... W , such that Y. W > 0 for all Y in y ' . For a fixed solution vector W let min Y. WA a Yey ' ( 5.11 ) where a > 0 ...
... W ; < 0 , we have from Eq . ( 5.8 ) Ŵk + 1 = 1 Ŵ1 + Ŷ1 + Ŷ1⁄2 + + Ŷ 2 ( 5.9 ) We shall prove the theorem for the ... W , such that Y. W > 0 for all Y in y ' . For a fixed solution vector W let min Y. WA a Yey ' ( 5.11 ) where a > 0 ...
Sivu 85
... W in W and each Y in y ' Here W is an open convex region bounded by hyperplanes ( the pattern hyperplanes ) all of which pass through the origin . Such a region is called a convex polyhedral cone with vertex at the origin . If a ...
... W in W and each Y in y ' Here W is an open convex region bounded by hyperplanes ( the pattern hyperplanes ) all of which pass through the origin . Such a region is called a convex polyhedral cone with vertex at the origin . If a ...
Sivu 92
... W│≤ Ŵx - W│ k ( 5.38 ) for all W in W. We therefore say that Ŵ + 1 is pointwise closer than Ŵx to W. As a first step in proving the theorem , we shall show that the sequence S converges to a point P. For any fixed W in W let lim Wx ...
... W│≤ Ŵx - W│ k ( 5.38 ) for all W in W. We therefore say that Ŵ + 1 is pointwise closer than Ŵx to W. As a first step in proving the theorem , we shall show that the sequence S converges to a point P. For any fixed W in W let lim Wx ...
Sisältö
TRAINABLE PATTERN CLASSIFIERS | 1 |
PARAMETRIC TRAINING METHODS | 43 |
SOME NONPARAMETRIC TRAINING METHODS | 65 |
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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 |