Learning Machines: Foundations of Trainable Pattern-classifying Systems |
Kirjan sisältä
Tulokset 1 - 3 kokonaismäärästä 7
Sivu
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
pattern ...
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
pattern ...
Sivu 4
Before operating a pattern classifier to forecast the weather , we must first decide
which d measurements to use as the input pattern . If d can be very large , we
might not need to exercise much care in the selection of measurements because
it ...
Before operating a pattern classifier to forecast the weather , we must first decide
which d measurements to use as the input pattern . If d can be very large , we
might not need to exercise much care in the selection of measurements because
it ...
Sivu 12
The problem of selection of measurements has also received some attention by
both statisticians and engineers . Bahadur , 8 Lewis , ' and Marill and Green 10
propose and discuss tests for the " effectiveness ” of measurements . Miller11 ...
The problem of selection of measurements has also received some attention by
both statisticians and engineers . Bahadur , 8 Lewis , ' and Marill and Green 10
propose and discuss tests for the " effectiveness ” of measurements . Miller11 ...
Mitä ihmiset sanovat - Kirjoita arvostelu
Yhtään arvostelua ei löytynyt.
Sisältö
Preface vii | 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 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 given implemented important initial layered machine linear dichotomies linear machine linearly separable matrix 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 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 |
| Pituus | 137 sivua |
| Vie sitaatti | BiBTeX EndNote RefMan |