Learning Machines: Foundations of Trainable Pattern-classifying SystemsMcGraw-Hill, 1965 - 137 sivua |
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Tulokset 1 - 3 kokonaismäärästä 32
Sivu 55
... pattern space . A set of normal patterns would then tend to be grouped in an ellipsoidal cluster centered around a prototype pattern M. 3.8 The optimum classifier for normal patterns We are now ready to derive the optimum classifier for ...
... pattern space . A set of normal patterns would then tend to be grouped in an ellipsoidal cluster centered around a prototype pattern M. 3.8 The optimum classifier for normal patterns We are now ready to derive the optimum classifier for ...
Sivu 57
... patterns belonging to a single category is a hyper- spherical cluster and each category is a priori equally probable . Then Eq . ( 3.33 ) could be written as ... patterns in PARAMETRIC TRAINING METHODS 57 Training with normal pattern sets,
... patterns belonging to a single category is a hyper- spherical cluster and each category is a priori equally probable . Then Eq . ( 3.33 ) could be written as ... patterns in PARAMETRIC TRAINING METHODS 57 Training with normal pattern sets,
Sivu 62
... normal distribution is well treated in a book by Anderson . Equation ( 3:31 ) for the quadric discriminant functions , opti- mum for normal patterns , has been previously derived by Anderson and others . A similar derivation motivated ...
... normal distribution is well treated in a book by Anderson . Equation ( 3:31 ) for the quadric discriminant functions , opti- mum for normal patterns , has been previously derived by Anderson and others . A similar derivation motivated ...
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 |