Learning Machines: Foundations of Trainable Pattern-classifying SystemsMcGraw-Hill, 1965 - 137 sivua |
Kirjan sisältä
Tulokset 1 - 3 kokonaismäärästä 32
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 103
... properties of layered machines We have seen in Secs . 6-2 to 6-4 that the concept of the first - layer TLUS as voters in a " committee " is a productive representation for two - layer machines . Another representation , to be discussed ...
... properties of layered machines We have seen in Secs . 6-2 to 6-4 that the concept of the first - layer TLUS as voters in a " committee " is a productive representation for two - layer machines . Another representation , to be discussed ...
Sisältö
TRAINABLE PATTERN CLASSIFIERS | 1 |
SOME NONPARAMETRIC TRAINING METHODS | 65 |
TRAINING THEOREMS | 79 |
Tekijänoikeudet | |
2 muita osia ei näytetty
Muita painoksia - Näytä kaikki
Yleiset termit ja lausekkeet
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 |