Learning Machines: Foundations of Trainable Pattern-classifying Systems

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McGraw-Hill, 1965 - 137 sivua
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A forecast must be based on certain weather measurements , for example , the present values of atmospheric pressure and atmospheric pressure changes at a number of stations . Suppose that today the forecaster wishes to predict whether ...
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The adjustments can occur after the machine is constructed by making changes in the organization , structure , or parameter values of the parts of the machine , or it can occur before hardware construction by making these changes on a ...
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The value of the present probabilistic model is that the unconditional density function for X changes as a result of being given a set of training patterns . We can see how it changes by first calculating an a posteriori density ...

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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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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

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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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