Learning Machines: Foundations of Trainable Pattern-classifying Systems |
Kirjan sisältä
Tulokset 1 - 3 kokonaismäärästä 19
Sivu 9
It is known a priori that the pattern points in category 1 tend to cluster close to some central cluster point X1 , and that the pattern points in category 2 tend to cluster close to another cluster point X2 .
It is known a priori that the pattern points in category 1 tend to cluster close to some central cluster point X1 , and that the pattern points in category 2 tend to cluster close to another cluster point X2 .
Sivu 52
They will tend to be grouped in an ellipsoidal cluster centered around the point ( m1 , m2 ) . Several such ellipsoidal clusters of pattern points are illustrated in Fig . 3.3 . One cluster might contain patterns belonging to category 1 ...
They will tend to be grouped in an ellipsoidal cluster centered around the point ( m1 , m2 ) . Several such ellipsoidal clusters of pattern points are illustrated in Fig . 3.3 . One cluster might contain patterns belonging to category 1 ...
Sivu 133
... of a quadric surface , 40 Center of gravity of a cluster , 53 Charnes , 92 , 93 Clark , 76 , 77 Classification , linear ... 122 Cluster point , 9 , 43 Clustering transformations , 12 , 131 Clusters , ellipsoidal , 52 , 131 center of ...
... of a quadric surface , 40 Center of gravity of a cluster , 53 Charnes , 92 , 93 Clark , 76 , 77 Classification , linear ... 122 Cluster point , 9 , 43 Clustering transformations , 12 , 131 Clusters , ellipsoidal , 52 , 131 center of ...
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 |