Learning Machines: Foundations of Trainable Pattern-classifying Systems |
Kirjan sisältä
Tulokset 1 - 3 kokonaismäärästä 21
Sivu 6
the decision surfaces divide Ed into R regions which we shall call decision
regions . The ith region Riis the set of points which map into the ith category
number . For convenience , we shall arbitrarily assume that patterns which lie on
decision ...
the decision surfaces divide Ed into R regions which we shall call decision
regions . The ith region Riis the set of points which map into the ith category
number . For convenience , we shall arbitrarily assume that patterns which lie on
decision ...
Sivu 19
There are R ( R - 1 ) / 2 such equations and thus the same number of surfaces Sij
. For d = 2 , a linear surface is called a line ; for d = 3 , a plane ; and for d > 3 , a
hyperplane . Thus , the decision surfaces of a linear machine are segments of at
...
There are R ( R - 1 ) / 2 such equations and thus the same number of surfaces Sij
. For d = 2 , a linear surface is called a line ; for d = 3 , a plane ; and for d > 3 , a
hyperplane . Thus , the decision surfaces of a linear machine are segments of at
...
Sivu 119
If the covariance matrices of the normal densities are equal , then the decision
surface which minimizes the probability of error is a hyperplane perpendicular to
the line segment joining the means of the two density functions . It should be ...
If the covariance matrices of the normal densities are equal , then the decision
surface which minimizes the probability of error is a hyperplane perpendicular to
the line segment joining the means of the two density functions . It should be ...
Mitä ihmiset sanovat - Kirjoita arvostelu
Yhtään arvostelua ei löytynyt.
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 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 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 specific Stanford step Suppose theorem theory threshold training methods training patterns training procedure training sequence training subsets transformation values weight vectors 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 |
| ISBN | 0070465703, 9780070465701 |
| Pituus | 137 sivua |
| Vie sitaatti | BiBTeX EndNote RefMan |