Learning Machines: Foundations of Trainable Pattern-classifying Systems |
Kirjan sisältä
Tulokset 1 - 3 kokonaismäärästä 25
Sivu 5
We shall not consider in this book any mappings requiring an infinite number of
decision surfaces or any mappings that cannot be described by such surfaces . *
In general , * The mapping which takes all points having one or more irrational ...
We shall not consider in this book any mappings requiring an infinite number of
decision surfaces or any mappings that cannot be described by such surfaces . *
In general , * The mapping which takes all points having one or more irrational ...
Sivu 24
2 : 7 Piecewise linear discriminant functions As a special case of discriminant
functions which we shall call piecewise linear , we shall first consider those of a
minimum - distance classifier with respect to point sets . Suppose we are given R
...
2 : 7 Piecewise linear discriminant functions As a special case of discriminant
functions which we shall call piecewise linear , we shall first consider those of a
minimum - distance classifier with respect to point sets . Suppose we are given R
...
Sivu 97
Consider the three pattern hyperplanes ( lines ) in Fig . ... As an example of a
nonlinear dichotomy , consider the one given by the subsets Yi = { Y1 , Y2 } Y2 =
Y ( 6•2 ) None of the regions formed by these three pattern hyperplanes contains
a ...
Consider the three pattern hyperplanes ( lines ) in Fig . ... As an example of a
nonlinear dichotomy , consider the one given by the subsets Yi = { Y1 , Y2 } Y2 =
Y ( 6•2 ) None of the regions formed by these three pattern hyperplanes contains
a ...
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 |