Learning Machines: Foundations of Trainable Pattern-classifying Systems

Etukansi
McGraw-Hill, 1965 - 137 sivua
0 Arvostelut

Kirjan sisältä

 

Tulokset 1 - 3 kokonaismäärästä 31

Sivu 5
Any pattern can be represented by a point in a d - dimensional Euclidean space
Ed called the pattern space . ... denote both the pattern point and the pattern
vector by the symbol X . A pattern classifier is thus a device which maps the
points of ...
Sivu 32
We shall show that if the positions of the N pattern points satisfy some quite mild
conditions , the number of dichotomies that can be implemented by a o function
will depend only on the number of patterns N and the number of parameters M +
1 ...
Sivu 36
Suppose that we have a set X of N points and a set Z of K points ( K < d ) in Ed .
We desire to know the number Lz ( N , d ) of linear dichotomies of X achievable
by a hyperplane constrained to contain all the points of Z . We shall assume that ...

Missä on loput tästä teoksesta?

Mitä ihmiset sanovat - Kirjoita arvostelu

Yhtään arvostelua ei löytynyt.

Sisältö

Preface vii
1
SOME NONPARAMETRIC TRAINING METHODS
65
TRAINING THEOREMS
79
Tekijänoikeudet

2 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 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 reduced 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

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
Pituus137 sivua
  
Vie sitaattiBiBTeX EndNote RefMan