Learning Machines: Foundations of Trainable Pattern-classifying SystemsMcGraw-Hill, 1965 - 137 sivua |
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Tulokset 1 - 3 kokonaismäärästä 21
Sivu 9
... cluster close to some central cluster point X1 , and that the pattern points in category 2 tend to cluster close to another cluster point X2 . The coordinates of the points X1 and X2 constitute the parameters of the pat- tern sets . The ...
... cluster close to some central cluster point X1 , and that the pattern points in category 2 tend to cluster close to another cluster point X2 . The coordinates of the points X1 and X2 constitute the parameters of the pat- tern sets . The ...
Sivu 52
... cluster centered around the point ( m1 , m2 ) . Several such ellipsoidal clusters of pattern points are illustrated in Fig . 3.3 . One cluster might contain patterns belonging to category 1 ; another might contain patterns belonging to ...
... cluster centered around the point ( m1 , m2 ) . Several such ellipsoidal clusters of pattern points are illustrated in Fig . 3.3 . One cluster might contain patterns belonging to category 1 ; another might contain patterns belonging to ...
Sivu 133
... Cluster point , 9 , 43 Clustering transformations , 12 , 131 Clusters , ellipsoidal , 52 , 131 center of gravity of , 53 spherical , 131 Committee machines , 97 , 107 example of training , 101 training method for , 99 Constraints ...
... Cluster point , 9 , 43 Clustering transformations , 12 , 131 Clusters , ellipsoidal , 52 , 131 center of gravity of , 53 spherical , 131 Committee machines , 97 , 107 example of training , 101 training method for , 99 Constraints ...
Sisältö
TRAINABLE PATTERN CLASSIFIERS | 1 |
PARAMETRIC TRAINING METHODS | 43 |
SOME NONPARAMETRIC TRAINING METHODS | 65 |
Tekijänoikeudet | |
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adjusted apply assume bank belonging to category called changes Chapter cluster committee components consider consists contains correction corresponding decision surfaces define denote density depends derivation described Development discriminant functions discussed distance distribution element equal error-correction estimates example exists expression FIGURE fixed given implemented important initial layered machine linear dichotomies linear discriminant functions linear machine linearly separable measurements negative networks normal Note optimum origin parameters partition pattern classifier pattern hyperplane pattern space pattern vector piecewise linear plane points positive presented probability problem proof properties proved PWL machine quadric reduced regions respect response rule sample mean selection separable shown side space Stanford step subsidiary discriminant Suppose theorem theory threshold training methods training procedure training sequence training subsets transformation values weight vectors zero
Viitteet tähän teokseen
A Probabilistic Theory of Pattern Recognition Luc Devroye,László Györfi,Gabor Lugosi Rajoitettu esikatselu - 1997 |