Learning Machines: Foundations of Trainable Pattern-classifying SystemsMcGraw-Hill, 1965 - 137 sivua |
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Tulokset 1 - 3 kokonaismäärästä 29
Sivu vii
... properties of various discriminant functions or to find methods for their selection or adjustment . The following topics are given special treatment : 1. Parametric and nonparametric training methods . The decision- theoretic approach ...
... properties of various discriminant functions or to find methods for their selection or adjustment . The following topics are given special treatment : 1. Parametric and nonparametric training methods . The decision- theoretic approach ...
Sivu 11
... properties of some in detail , and present block diagrams to suggest the manner in which they might be employed . Chapter 3 will investigate decision - theoretic parametric training methods . The mathematical foundation underlying these ...
... properties of some in detail , and present block diagrams to suggest the manner in which they might be employed . Chapter 3 will investigate decision - theoretic parametric training methods . The mathematical foundation underlying these ...
Sivu 16
... properties , leaving the subject of training to later chapters . One of the simplest is the family of linear functions to which we now turn . 2.2 Linear discriminant functions Let us consider first the family of discriminant functions ...
... properties , leaving the subject of training to later chapters . One of the simplest is the family of linear functions to which we now turn . 2.2 Linear discriminant functions Let us consider first the family of discriminant functions ...
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 |