Learning Machines: Foundations of Trainable Pattern-classifying SystemsMcGraw-Hill, 1965 - 137 sivua |
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Sivu 12
... contains an excellent formulation of the pattern - classification problem and also points out that many schemes currently attracting the attention of engineers have antecedents in the statistical literature . The problem of data ...
... contains an excellent formulation of the pattern - classification problem and also points out that many schemes currently attracting the attention of engineers have antecedents in the statistical literature . The problem of data ...
Sivu 36
... contain all the points of Z. We shall assume that the points of Z are in general position , meaning , in this case , that no ( K – 2 ) -dimensional hyperplane contains all of them . The set Z The set consists of three points on a line ...
... contain all the points of Z. We shall assume that the points of Z are in general position , meaning , in this case , that no ( K – 2 ) -dimensional hyperplane contains all of them . The set Z The set consists of three points on a line ...
Sivu 105
... 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 corresponds to a vertex in 1 space . Thus , the four ...
... 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 corresponds to a vertex in 1 space . Thus , the four ...
Sisältö
TRAINABLE PATTERN CLASSIFIERS | 1 |
PARAMETRIC TRAINING METHODS | 43 |
SOME NONPARAMETRIC TRAINING METHODS | 65 |
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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 |