Learning Machines: Foundations of Trainable Pattern-classifying Systems

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McGraw-Hill, 1965 - 137 sivua
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Tulokset 1 - 3 kokonaismäärästä 22

Sivu 24
Let us define the Euclidean distance d ( X , P ; ) from an arbi- trary point X to the point set P ; by ... " d ( X , P ; ) = i min j = 1 , ... , Li X - P , i ( 2.16 ) That is , the distance between X and P ; is the smallest of the ...
Sivu 47
... we define the discriminant function , g ( X ) = g1 ( X ) — 92 ( X ) . If g ( X ) > 0 , the machine places X in category 1 ; if g ( X ) < 0 , the machine places X in category 2. From Eq . ( 3-76 ) we can derive g ( X ) = log p ( X1 ) ...
Sivu 128
Let us now define the real , diagonal matrices D1 = λι 0 and ( A - 4 ) 0 where A1 , .. D2 0 Pi Ap , are the first p1 diagonal elements of A , and Ap1 + 1 , App , are the next p2 diagonal elements of A. 1 Now let T1 be a dX p1 matrix ...

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Sisältö

TRAINABLE PATTERN CLASSIFIERS
1
PARAMETRIC TRAINING METHODS
43
SOME NONPARAMETRIC TRAINING METHODS
65
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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
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Yleiset termit ja lausekkeet

adjusted apply assume bank called cells changes Chapter classifier cluster column committee machine components consider consists contains correction corresponding covariance decision surfaces define denote density depends described discriminant functions discussed distance distributions elements equal error-correction estimates example exist expression FIGURE fixed given implemented initial layered machine linear machine linearly separable lines majority matrix mean measurements modes negative networks nonparametric normal Note optimum origin parameters partition pattern hyperplane pattern space pattern vector pattern-classifying piecewise linear plane points positive presented probability problem properties PWL machine quadric regions respect response rule selection separable sequence side solution space step subsidiary discriminant Suppose terns theorem theory threshold training methods training patterns training procedure training sequence training subsets transformation values weight vectors X1 and X2 Y₁ zero

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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
Alkuperäisteoksen sijaintiWisconsinin yliopisto - Madison
Digitoitu16. huhtikuu 2008
ISBN0070465703, 9780070465701
Pituus137 sivua
  
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