Learning Machines: Foundations of Trainable Pattern-classifying Systems |
Kirjan sisältä
Tulokset 1 - 3 kokonaismäärästä 11
Sivu 81
A slightly modified theorem can also be proved quite simply as a result of Theorem 5.1 . In the modified theorem we use an absolute errorcorrection procedure instead of the fixed - increment error - correction procedure .
A slightly modified theorem can also be proved quite simply as a result of Theorem 5.1 . In the modified theorem we use an absolute errorcorrection procedure instead of the fixed - increment error - correction procedure .
Sivu 84
... be no larger than km , which is a solution to the equation kmM = km ? a2 W 2 or km = MW12 a ? ( 5.21 ) Therefore , we have proved ( for W , = 0 ) that the fixed - increment error - correction procedure must terminate after at most ...
... be no larger than km , which is a solution to the equation kmM = km ? a2 W 2 or km = MW12 a ? ( 5.21 ) Therefore , we have proved ( for W , = 0 ) that the fixed - increment error - correction procedure must terminate after at most ...
Sivu 93
Block has proved a generalized version of Theorem 5.1 in which the correction increment Ck of Eq . ( 5.4 ) need not be independent of k . Theorem 5.2 was first proved by C. Kesler at Cornell University .
Block has proved a generalized version of Theorem 5.1 in which the correction increment Ck of Eq . ( 5.4 ) need not be independent of k . Theorem 5.2 was first proved by C. Kesler at Cornell University .
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 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 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
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 |