Learning Machines: Foundations of Trainable Pattern-classifying Systems |
Kirjan sisältä
Tulokset 1 - 3 kokonaismäärästä 21
Sivu 87
5 A training theorem for R - category linear machines Suppose we are given R
finite subsets of augmented training pattern vectors Yı , Y2 , . . . , Yr . These
subsets are linearly separable if and only if there exist R solution weight vectors
W1 ...
5 A training theorem for R - category linear machines Suppose we are given R
finite subsets of augmented training pattern vectors Yı , Y2 , . . . , Yr . These
subsets are linearly separable if and only if there exist R solution weight vectors
W1 ...
Sivu 119
It should be observed that if the two density functions overlap sufficiently , it is
likely that this optimum decision surface will not perfectly separate all the
members of the two training subsets . If we were willing to assume initially that
these ...
It should be observed that if the two density functions overlap sufficiently , it is
likely that this optimum decision surface will not perfectly separate all the
members of the two training subsets . If we were willing to assume initially that
these ...
Sivu 120
function that depends on the geometric arrangement of the patterns in the
training subsets . Many of these nonparametric rules actually lead to the same
discriminant functions that would be obtained by parametric training and the
assumptions ...
function that depends on the geometric arrangement of the patterns in the
training subsets . Many of these nonparametric rules actually lead to the same
discriminant functions that would be obtained by parametric training and the
assumptions ...
Mitä ihmiset sanovat - Kirjoita arvostelu
Yhtään arvostelua ei löytynyt.
Sisältö
Preface vii | 7 |
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 belonging to category called changes Chapter cluster committee components consider consists contains correction corresponding covariance decision surfaces define denote density depends derivation described Development discriminant functions discussed distance distribution element equal error-correction estimates example exists expression FIGURE fixed gi(X given implemented important initial layered machine linear dichotomies linear machine linearly separable matrix measurements 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 solution space Stanford step Suppose theorem theory threshold training methods training procedure training sequence training subsets transformation values weight vectors zero
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 |
| Pituus | 137 sivua |
| Vie sitaatti | BiBTeX EndNote RefMan |