Learning Machines: Foundations of Trainable Pattern-classifying Systems |
Kirjan sisältä
Tulokset 1 - 3 kokonaismäärästä 7
Sivu viii
An example of a deliberate omission is the subject of measurement selection , sometimes called preprocessing . The selection of the measurements or properties on which recognition is based is one of the most important problems in ...
An example of a deliberate omission is the subject of measurement selection , sometimes called preprocessing . The selection of the measurements or properties on which recognition is based is one of the most important problems in ...
Sivu 4
If d can be very large , we might not need to exercise much care in the selection of measurements because it is likely ... reasons ) d is smaller than we might like it to be , the problem of measurement selection is a pressing one .
If d can be very large , we might not need to exercise much care in the selection of measurements because it is likely ... reasons ) d is smaller than we might like it to be , the problem of measurement selection is a pressing one .
Sivu 12
8 9 The problem of selection of measurements has also received some attention by both statisticians and engineers . Bahadur , Lewis , and Marill and Green 10 propose and discuss tests for the " effectiveness " of measurements .
8 9 The problem of selection of measurements has also received some attention by both statisticians and engineers . Bahadur , Lewis , and Marill and Green 10 propose and discuss tests for the " effectiveness " of measurements .
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 |