MARC details
| 000 -LIDER |
|---|
| fixed length control field |
04508nam a22005895i 4500 |
| 001 - CONTROL NUMBER |
|---|
| control field |
978-3-319-17290-3 |
| 003 - CONTROL NUMBER IDENTIFIER |
|---|
| control field |
DE-He213 |
| 005 - DATE AND TIME OF LATEST TRANSACTION |
|---|
| control field |
20180206183022.0 |
| 007 - PHYSICAL DESCRIPTION FIXED FIELD--GENERAL INFORMATION |
|---|
| fixed length control field |
cr nn 008mamaa |
| 008 - FIXED-LENGTH DATA ELEMENTS--GENERAL INFORMATION |
|---|
| fixed length control field |
160128s2016 gw | s |||| 0|eng d |
| 020 ## - INTERNATIONAL STANDARD BOOK NUMBER |
|---|
| International Standard Book Number |
9783319172903 |
| -- |
978-3-319-17290-3 |
| 050 #4 - LIBRARY OF CONGRESS CALL NUMBER |
|---|
| Classification number |
Q334-342 |
| 050 #4 - LIBRARY OF CONGRESS CALL NUMBER |
|---|
| Classification number |
TJ210.2-211.495 |
| 072 #7 - SUBJECT CATEGORY CODE |
|---|
| Subject category code |
UYQ |
| Source |
bicssc |
| 072 #7 - SUBJECT CATEGORY CODE |
|---|
| Subject category code |
TJFM1 |
| Source |
bicssc |
| 072 #7 - SUBJECT CATEGORY CODE |
|---|
| Subject category code |
COM004000 |
| Source |
bisacsh |
| 082 04 - DEWEY DECIMAL CLASSIFICATION NUMBER |
|---|
| Classification number |
006.3 |
| Edition number |
23 |
| 100 1# - MAIN ENTRY--PERSONAL NAME |
|---|
| Personal name |
Christiano Silva, Thiago. |
| Relator term |
author. |
| 245 10 - TITLE STATEMENT |
|---|
| Title |
Machine Learning in Complex Networks |
| Medium |
[recurso electrónico] / |
| Statement of responsibility, etc. |
by Thiago Christiano Silva, Liang Zhao. |
| 250 ## - EDITION STATEMENT |
|---|
| Edition statement |
1st ed. 2016. |
| 264 #1 - |
|---|
| -- |
Cham : |
| -- |
Springer International Publishing : |
| -- |
Imprint: Springer, |
| -- |
2016. |
| 300 ## - PHYSICAL DESCRIPTION |
|---|
| Extent |
XVIII, 331 p. 87 illus., 80 illus. in color. |
| Other physical details |
online resource. |
| 336 ## - |
|---|
| -- |
text |
| -- |
txt |
| -- |
rdacontent |
| 337 ## - |
|---|
| -- |
computer |
| -- |
c |
| -- |
rdamedia |
| 338 ## - |
|---|
| -- |
online resource |
| -- |
cr |
| -- |
rdacarrier |
| 347 ## - |
|---|
| -- |
text file |
| -- |
PDF |
| -- |
rda |
| 505 0# - FORMATTED CONTENTS NOTE |
|---|
| Formatted contents note |
Introduction -- Complex Networks -- Machine Learning -- Network Construction Techniques -- Network-Based Supervised Learning -- Network-Based Unsupervised Learning -- Network-Based Semi-Supervised Learning -- Case Study of Network-Based Supervised Learning: High-Level Data Classification -- Case Study of Network-Based Unsupervised Learning: Stochastic Competitive Learning in Networks -- Case Study of Network-Based Semi-Supervised Learning: Stochastic Competitive-Cooperative Learning in Networks. |
| 520 ## - SUMMARY, ETC. |
|---|
| Summary, etc. |
This book explores the features and advantages offered by complex networks in the domain of machine learning. In the first part of the book, we present an overview on complex networks and machine learning. Then, we provide a comprehensive description on network-based machine learning. In addition, we also address the important network construction issue. In the second part of the book, we describe some techniques for supervised, unsupervised, and semi-supervised learning that rely on complex networks to perform the learning process. Particularly, we thoroughly investigate a particle competition technique for both unsupervised and semi-supervised learning that is modeled using a stochastic nonlinear dynamical system. Moreover, we supply an analytical analysis of the model, which enables one to predict the behavior of the proposed technique. In addition, we deal with data reliability issues or imperfect data in semi-supervised learning. Even though with relevant practical importance, little research is found about this topic in the literature. In order to validate these techniques, we employ broadly accepted real-world and artificial data sets. Regarding network-based supervised learning, we present a hybrid data classification technique that combines both low and high orders of learning. The low-level term can be implemented by any traditional classification technique, while the high-level term is realized by the extraction of topological features of the underlying network constructed from the input data. Thus, the former classifies test instances according to their physical features, while the latter measures the compliance of test instances with the pattern formation of the data. We show that the high-level technique can realize classification according to the semantic meaning of the data. This book intends to combine two widely studied research areas, machine learning and complex networks, which in turn may generate broad interests to scientific community, mainly to computer science and engineering areas. |
| 650 #0 - SUBJECT ADDED ENTRY--TOPICAL TERM |
|---|
| Término temático o nombre geográfico como elemento de entrada |
Computer science. |
| 650 #0 - SUBJECT ADDED ENTRY--TOPICAL TERM |
|---|
| Término temático o nombre geográfico como elemento de entrada |
Science. |
| 650 #0 - SUBJECT ADDED ENTRY--TOPICAL TERM |
|---|
| Término temático o nombre geográfico como elemento de entrada |
Data mining. |
| 650 #0 - SUBJECT ADDED ENTRY--TOPICAL TERM |
|---|
| Término temático o nombre geográfico como elemento de entrada |
Artificial intelligence. |
| 650 #0 - SUBJECT ADDED ENTRY--TOPICAL TERM |
|---|
| Término temático o nombre geográfico como elemento de entrada |
Pattern recognition. |
| 650 #0 - SUBJECT ADDED ENTRY--TOPICAL TERM |
|---|
| Término temático o nombre geográfico como elemento de entrada |
Physics. |
| 650 #0 - SUBJECT ADDED ENTRY--TOPICAL TERM |
|---|
| Término temático o nombre geográfico como elemento de entrada |
Computational intelligence. |
| 650 14 - SUBJECT ADDED ENTRY--TOPICAL TERM |
|---|
| Término temático o nombre geográfico como elemento de entrada |
Computer Science. |
| 650 24 - SUBJECT ADDED ENTRY--TOPICAL TERM |
|---|
| Término temático o nombre geográfico como elemento de entrada |
Artificial Intelligence (incl. Robotics). |
| 650 24 - SUBJECT ADDED ENTRY--TOPICAL TERM |
|---|
| Término temático o nombre geográfico como elemento de entrada |
Computational Intelligence. |
| 650 24 - SUBJECT ADDED ENTRY--TOPICAL TERM |
|---|
| Término temático o nombre geográfico como elemento de entrada |
Complex Networks. |
| 650 24 - SUBJECT ADDED ENTRY--TOPICAL TERM |
|---|
| Término temático o nombre geográfico como elemento de entrada |
Science, general. |
| 650 24 - SUBJECT ADDED ENTRY--TOPICAL TERM |
|---|
| Término temático o nombre geográfico como elemento de entrada |
Data Mining and Knowledge Discovery. |
| 650 24 - SUBJECT ADDED ENTRY--TOPICAL TERM |
|---|
| Término temático o nombre geográfico como elemento de entrada |
Pattern Recognition. |
| 700 1# - ADDED ENTRY--PERSONAL NAME |
|---|
| Personal name |
Zhao, Liang. |
| Relator term |
author. |
| 710 2# - ADDED ENTRY--CORPORATE NAME |
|---|
| Corporate name or jurisdiction name as entry element |
SpringerLink (Online service) |
| 773 0# - HOST ITEM ENTRY |
|---|
| Title |
Springer eBooks |
| 776 08 - ADDITIONAL PHYSICAL FORM ENTRY |
|---|
| Relationship information |
Printed edition: |
| International Standard Book Number |
9783319172897 |
| 856 40 - ELECTRONIC LOCATION AND ACCESS |
|---|
| Public note |
Libro electrónico |
| Uniform Resource Identifier |
http://148.231.10.114:2048/login?url=http://dx.doi.org/10.1007/978-3-319-17290-3 |
| 912 ## - |
|---|
| -- |
ZDB-2-SCS |
| 942 ## - ADDED ENTRY ELEMENTS (KOHA) |
|---|
| Koha item type |
Libro Electrónico |
