Learning Machines: Foundations of Trainable Pattern-classifying SystemsMcGraw-Hill, 1965 - 137 sivua |
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Sivu 105
... cells . In this example there are four cells that contain pat- tern points . Two of these cells contain one pattern each ; one cell contains four patterns ; and one cell contains two patterns . Each nonempty cell in pattern space ...
... cells . In this example there are four cells that contain pat- tern points . Two of these cells contain one pattern each ; one cell contains four patterns ; and one cell contains two patterns . Each nonempty cell in pattern space ...
Sivu 106
... cells such that no two patterns of opposite cate- gorization reside in the same cell . The necessity for partitioning the sets X1 and X2 arises because corresponding to each nonempty cell in the pat- tern space is a vertex in 11 space ...
... cells such that no two patterns of opposite cate- gorization reside in the same cell . The necessity for partitioning the sets X1 and X2 arises because corresponding to each nonempty cell in the pat- tern space is a vertex in 11 space ...
Sivu 108
... cells will merge into one cell . Some examples of nonredundant and redundant partitions are shown in Fig . 6.8 . Note that the partition shown in Fig . 6-7a is also nonredundant . A nonredundant partition is not necessarily one that ...
... cells will merge into one cell . Some examples of nonredundant and redundant partitions are shown in Fig . 6.8 . Note that the partition shown in Fig . 6-7a is also nonredundant . A nonredundant partition is not necessarily one that ...
Sisältö
TRAINABLE PATTERN CLASSIFIERS | 1 |
SOME NONPARAMETRIC TRAINING METHODS | 65 |
TRAINING THEOREMS | 79 |
Tekijänoikeudet | |
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adjusted apply assume bank called cells changes Chapter cluster column committee machine components consider consists contains correction corresponding covariance decision surfaces define denote density depends described dichotomies discriminant functions discussed distance distributions elements equal error-correction estimates example exist expression FIGURE fixed given implemented important initial layered machine linear machine linearly separable lines majority matrix mean measurements modes negative networks nonparametric normal Note optimum origin parameters partition pattern classifier pattern hyperplane pattern space pattern vector 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 |