Learning Machines: Foundations of Trainable Pattern-classifying Systems

Etukansi
McGraw-Hill, 1965 - 137 sivua
0 Arvostelut
Arvosteluja ei vahvisteta, mutta Google tarkistaa ne valheellisen sisällön varalta ja poistaa tällaisen sisällön

Kirjan sisältä

 

Tulokset 1 - 3 kokonaismäärästä 20

Sivu 11
These theorems apply to a large ... do not belong to the class of functions for which the theorems of Chapter 5 apply ; nevertheless , there do exist some useful training procedures for layered machines which shall be discussed .
Sivu 90
We apply this rule to each element of Sý to generate the sequence Sz . The final step of the proof is to form a sequence Sy of RD - dimensional weight vectors from the reduced weight - vector sequences , SW , Let Vk be the kth member of ...
Sivu 122
What is needed to apply the closest - mode method is a means of training a PWL machine such that the modes are identified and the appropriate discriminant functions are set up . This training process should be an iterative one ...

Missä on loput tästä teoksesta?

Mitä ihmiset sanovat - Kirjoita arvostelu

Yhtään arvostelua ei löytynyt.

Sisältö

I
1
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 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

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