Learning Machines: Foundations of Trainable Pattern-classifying Systems |
Kirjan sisältä
Tulokset 1 - 3 kokonaismäärästä 19
Sivu 11
These theorems apply to a large class of discriminant functions and are therefore of funda- mental importance . The concept of a layered machine is introduced in Chapter 6. Most of the pattern classifiers containing threshold elements ...
These theorems apply to a large class of discriminant functions and are therefore of funda- mental importance . The concept of a layered machine is introduced in Chapter 6. Most of the pattern classifiers containing threshold elements ...
Sivu 90
We apply this rule to each element of Sy to generate the sequence Sz . The final step of the proof is to form a sequence Sy of RD - dimensional weight vectors from the reduced weight - vector sequences , Sŵ ,, . . . , SŴR .
We apply this rule to each element of Sy to generate the sequence Sz . The final step of the proof is to form a sequence Sy of RD - dimensional weight vectors from the reduced weight - vector sequences , Sŵ ,, . . . , SŴR .
Sivu 122
What is needed to apply the closest - mode method is a means of training a PWL machine such that the modes are identified and the appropriate discriminant functions are set up . This training process should be an iterative one ...
What is needed to apply the closest - mode method is a means of training a PWL machine such that the modes are identified and the appropriate discriminant functions are set up . This training process should be an iterative one ...
Mitä ihmiset sanovat - Kirjoita arvostelu
Yhtään arvostelua ei löytynyt.
Sisältö
TRAINABLE PATTERN CLASSIFIERS | 1 |
PARAMETRIC TRAINING METHODS | 43 |
SOME NONPARAMETRIC TRAINING METHODS | 65 |
Tekijänoikeudet | |
3 muita osia ei näytetty
Muita painoksia - Näytä kaikki
Yleiset termit ja lausekkeet
adjusted apply assume bank called cells changes Chapter classifier cluster column committee machine components consider consists contains correction corresponding covariance decision surfaces define denote density depends described discriminant functions discussed distance distributions elements equal error-correction estimates example exist expression FIGURE fixed given implemented initial layered machine linear machine linearly separable lines majority matrix mean measurements modes negative networks nonparametric normal Note optimum origin parameters partition pattern hyperplane pattern space pattern vector pattern-classifying piecewise linear plane points positive presented probability problem properties PWL machine quadric regions respect response rule selection separable sequence side solution space step subsidiary discriminant Suppose terns 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 |
Kirjaluettelon tiedot
| Otsikko | Learning Machines: Foundations of Trainable Pattern-classifying Systems McGraw-Hill series in systems science |
| Kirjoittaja | Nils J. Nilsson |
| Painos | kuvitettu |
| Kustantaja | McGraw-Hill, 1965 |
| Alkuperäisteoksen sijainti | Wisconsinin yliopisto - Madison |
| Digitoitu | 16. huhtikuu 2008 |
| ISBN | 0070465703, 9780070465701 |
| Pituus | 137 sivua |
| Vie sitaatti | BiBTeX EndNote RefMan |