Learning Machines: Foundations of Trainable Pattern-classifying SystemsMcGraw-Hill, 1965 - 137 sivua |
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Tulokset 1 - 3 kokonaismäärästä 20
Sivu 20
... side and each member of X2 on the other side . Because the decision regions of a linear machine are convex 20 SOME IMPORTANT DISCRIMINANT FUNCTIONS Linear classifications of patterns,
... side and each member of X2 on the other side . Because the decision regions of a linear machine are convex 20 SOME IMPORTANT DISCRIMINANT FUNCTIONS Linear classifications of patterns,
Sivu 67
... side of the hyperplane , called the positive side , and those which produce a TLU response of minus one are on the other , or negative side . Note that the point representing the weight values w1 = 0 , w2 = 0 , O satisfies Eq . ( 4 · 2 ) ...
... side of the hyperplane , called the positive side , and those which produce a TLU response of minus one are on the other , or negative side . Note that the point representing the weight values w1 = 0 , w2 = 0 , O satisfies Eq . ( 4 · 2 ) ...
Sivu 69
... side of or on the pattern hyperplane corresponding to Y. This error can be rectified by moving W to the posi- tive side of the pattern hyperplane . The most direct path to the other side is along a line normal to the pattern hyperplane ...
... side of or on the pattern hyperplane corresponding to Y. This error can be rectified by moving W to the posi- tive side of the pattern hyperplane . The most direct path to the other side is along a line normal to the pattern hyperplane ...
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 |