Learning Machines: Foundations of Trainable Pattern-classifying Systems

Etukansi
McGraw-Hill, 1965 - 137 sivua
0 Arvostelut

Kirjan sisältä

 

Tulokset 1 - 3 kokonaismäärästä 27

Sivu 49
Note , for example , that the values of the a priori probabilities p ( 1 ) and 1 – p ( 1 ) affect only the value of wd + ... that there are circumstances in which it is possible to make optimum estimates of unknown probability values .
Sivu 54
There is a multivariate normal distribution which describes the joint probability density of d components . Patterns selected according to this joint probability distribution will be called multivariate normal patterns or , more simply ...
Sivu 136
... 76 of piecewise linear machines , 116 , 122 of TLUs , 65 Nonredundant partition , 107 Normal patterns , 52 , 54 mean vector of , 53 , 54 transformation of , 131 Normal probability - density function , bivariate , 50 equations of ...

Missä on loput tästä teoksesta?

Mitä ihmiset sanovat - Kirjoita arvostelu

Yhtään arvostelua ei löytynyt.

Sisältö

Preface vii
11
SOME NONPARAMETRIC TRAINING METHODS
65
APPENDIX
127
Tekijänoikeudet

1 muita osia ei näytetty

Muita painoksia - Näytä kaikki

Learning Machines: Foundations of Trainable Pattern-classifying Systems
Nils J. Nilsson
Otenäkymä - 1965
Learning Machines: Foundations of Trainable Pattern-classifying Systems
Nils J. Nilsson
Otenäkymä - 1965
Learning Machines: Foundations of Trainable Pattern-classifying Systems
Nils J. Nilsson
Otenäkymä - 1965
Näytä kaikki »

Yleiset termit ja lausekkeet

adjusted apply assume bank belonging to category called changes Chapter cluster committee components consider consists contains correction corresponding 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 known layered machine linear dichotomies linear machine linearly separable 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 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

Tietoja kirjailijasta (1965)

Nils John Nilsson was born in Saginaw, Michigan on February 6, 1933. He received a bachelor's degree and a Ph.D. in electrical engineering from Stanford University. He joined the Air Force and served three years at the Rome Air Development Center before joining the Stanford Research Institute in 1961. He helped develop the first general-purpose robot and was a co-inventor of algorithms that made it possible for the machine to move about efficiently and perform simple tasks. He later worked on the "Computer-based Consultant," which focused on natural language understanding. He wrote several books including Learning Machines: Foundations of Trainable Pattern-Classifying Systems and The Quest for Artificial Intelligence: A History of Ideas and Achievements. He died on April 21, 2019 at the age of 86.

Kirjaluettelon tiedot

QR code for Learning Machines
OtsikkoLearning Machines: Foundations of Trainable Pattern-classifying Systems
McGraw-Hill series in systems science
KirjoittajaNils J. Nilsson
Painoskuvitettu
KustantajaMcGraw-Hill, 1965
ISBN0070465703, 9780070465701
Pituus137 sivua
  
Vie sitaattiBiBTeX EndNote RefMan