| 000 -LEADER |
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| fixed length control field |
03288nam a22005055i 4500 |
| 001 - CONTROL NUMBER |
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| control field |
978-0-387-31240-8 |
| 003 - CONTROL NUMBER IDENTIFIER |
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| control field |
DE-He213 |
| 005 - DATE AND TIME OF LATEST TRANSACTION |
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| control field |
20161121230519.0 |
| 007 - PHYSICAL DESCRIPTION FIXED FIELD--GENERAL INFORMATION |
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| fixed length control field |
cr nn 008mamaa |
| 008 - FIXED-LENGTH DATA ELEMENTS--GENERAL INFORMATION |
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100301s2006 xxu| s |||| 0|eng d |
| 020 ## - INTERNATIONAL STANDARD BOOK NUMBER |
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| International Standard Book Number |
9780387312408 |
| -- |
978-0-387-31240-8 |
| 024 7# - OTHER STANDARD IDENTIFIER |
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| Standard number or code |
10.1007/0-387-31240-4 |
| Source of number or code |
doi |
| 050 #4 - LIBRARY OF CONGRESS CALL NUMBER |
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| Classification number |
QA75.5-76.95 |
| 072 #7 - SUBJECT CATEGORY CODE |
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| Subject category code |
UY |
| Source |
bicssc |
| 072 #7 - SUBJECT CATEGORY CODE |
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| Subject category code |
UYA |
| Source |
bicssc |
| 072 #7 - SUBJECT CATEGORY CODE |
|---|
| Subject category code |
COM014000 |
| Source |
bisacsh |
| 072 #7 - SUBJECT CATEGORY CODE |
|---|
| Subject category code |
COM031000 |
| Source |
bisacsh |
| 082 04 - DEWEY DECIMAL CLASSIFICATION NUMBER |
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| Classification number |
004.0151 |
| Edition number |
23 |
| 100 1# - MAIN ENTRY--PERSONAL NAME |
|---|
| Personal name |
Nikolaev, Nikolay Y. |
| Relator term |
author. |
| 245 10 - TITLE STATEMENT |
|---|
| Title |
Adaptive Learning of Polynomial Networks |
| Medium |
[electronic resource] : |
| Remainder of title |
Genetic Programming, Backpropagation and Bayesian Methods / |
| Statement of responsibility, etc. |
by Nikolay Y. Nikolaev, Hitoshi Iba. |
| 264 #1 - PRODUCTION, PUBLICATION, DISTRIBUTION, MANUFACTURE, AND COPYRIGHT NOTICE |
|---|
| Place of production, publication, distribution, manufacture |
Boston, MA : |
| Name of producer, publisher, distributor, manufacturer |
Springer US, |
| Date of production, publication, distribution, manufacture, or copyright notice |
2006. |
| 300 ## - PHYSICAL DESCRIPTION |
|---|
| Extent |
XIV, 316 p. |
| Other physical details |
online resource. |
| 336 ## - CONTENT TYPE |
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| Content type term |
text |
| Content type code |
txt |
| Source |
rdacontent |
| 337 ## - MEDIA TYPE |
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| Media type term |
computer |
| Media type code |
c |
| Source |
rdamedia |
| 338 ## - CARRIER TYPE |
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| Carrier type term |
online resource |
| Carrier type code |
cr |
| Source |
rdacarrier |
| 347 ## - DIGITAL FILE CHARACTERISTICS |
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| File type |
text file |
| Encoding format |
PDF |
| Source |
rda |
| 490 1# - SERIES STATEMENT |
|---|
| Series statement |
Genetic and Evolutionary Computation |
| 505 0# - FORMATTED CONTENTS NOTE |
|---|
| Formatted contents note |
Inductive Genetic Programming -- Tree-Like PNN Representations -- Fitness Functions and Landscapes -- Search Navigation -- Backpropagation Techniques -- Temporal Backpropagation -- Bayesian Inference Techniques -- Statistical Model Diagnostics -- Time Series Modelling -- Conclusions. |
| 520 ## - SUMMARY, ETC. |
|---|
| Summary, etc. |
This book provides theoretical and practical knowledge for develop� ment of algorithms that infer linear and nonlinear models. It offers a methodology for inductive learning of polynomial neural network mod� els from data. The design of such tools contributes to better statistical data modelling when addressing tasks from various areas like system identification, chaotic time-series prediction, financial forecasting and data mining. The main claim is that the model identification process involves several equally important steps: finding the model structure, estimating the model weight parameters, and tuning these weights with respect to the adopted assumptions about the underlying data distrib� ution. When the learning process is organized according to these steps, performed together one after the other or separately, one may expect to discover models that generalize well (that is, predict well). The book off'ers statisticians a shift in focus from the standard f- ear models toward highly nonlinear models that can be found by con� temporary learning approaches. Speciafists in statistical learning will read about alternative probabilistic search algorithms that discover the model architecture, and neural network training techniques that identify accurate polynomial weights. They wfil be pleased to find out that the discovered models can be easily interpreted, and these models assume statistical diagnosis by standard statistical means. Covering the three fields of: evolutionary computation, neural net� works and Bayesian inference, orients the book to a large audience of researchers and practitioners. 0 |
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| 650 ## - SUBJECT ADDED ENTRY--TOPICAL TERM |
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C |
| 650 ## - SUBJECT ADDED ENTRY--TOPICAL TERM |
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| Topical term or geographic name entry element |
A |
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C |
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T |
| 650 ## - SUBJECT ADDED ENTRY--TOPICAL TERM |
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| Topical term or geographic name entry element |
A |
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| Topical term or geographic name entry element |
C |
| 650 ## - SUBJECT ADDED ENTRY--TOPICAL TERM |
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| Topical term or geographic name entry element |
I |
| 700 ## - ADDED ENTRY--PERSONAL NAME |
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| Relator term |
author.2 |
| Personal name |
S |
| 710 ## - ADDED ENTRY--CORPORATE NAME |
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| Title of a work |
S |
| 773 ## - HOST ITEM ENTRY |
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| Relationship information |
P |
| 776 ## - ADDITIONAL PHYSICAL FORM ENTRY |
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| International Standard Book Number |
9780387312392 0 |
| Main entry heading |
G |
| 830 ## - SERIES ADDED ENTRY--UNIFORM TITLE |
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h |
| 856 ## - ELECTRONIC LOCATION AND ACCESS |
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| Host name |
Z |
| 912 ## - |
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C |
