Optimization Based Data Mining: Theory and Applications (Record no. 15117)

MARC details
000 -LEADER
fixed length control field 06455nam a22005175i 4500
001 - CONTROL NUMBER
control field 978-0-85729-504-0
003 - CONTROL NUMBER IDENTIFIER
control field DE-He213
005 - DATE AND TIME OF LATEST TRANSACTION
control field 20170628033423.0
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 110516s2011 xxk| s |||| 0|eng d
020 ## - INTERNATIONAL STANDARD BOOK NUMBER
International Standard Book Number 9780857295040
-- 978-0-85729-504-0
024 7# - OTHER STANDARD IDENTIFIER
Standard number or code 10.1007/978-0-85729-504-0
Source of number or code doi
050 #4 - LIBRARY OF CONGRESS CALL NUMBER
Classification number QA76.9.D343
072 #7 - SUBJECT CATEGORY CODE
Subject category code UNF
Source bicssc
072 #7 - SUBJECT CATEGORY CODE
Subject category code UYQE
Source bicssc
072 #7 - SUBJECT CATEGORY CODE
Subject category code COM021030
Source bisacsh
082 04 - DEWEY DECIMAL CLASSIFICATION NUMBER
Classification number 006.312
Edition number 23
100 1# - MAIN ENTRY--PERSONAL NAME
Personal name Shi, Yong.
Relator term author.
245 10 - TITLE STATEMENT
Title Optimization Based Data Mining: Theory and Applications
Medium [electronic resource] /
Statement of responsibility, etc by Yong Shi, Yingjie Tian, Gang Kou, Yi Peng, Jianping Li.
264 #1 -
-- London :
-- Springer London,
-- 2011.
300 ## - PHYSICAL DESCRIPTION
Extent XVI, 316 p.
Other physical details online resource.
336 ## -
-- text
-- txt
-- rdacontent
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-- computer
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-- rdamedia
338 ## -
-- online resource
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347 ## -
-- text file
-- PDF
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490 1# - SERIES STATEMENT
Series statement Advanced Information and Knowledge Processing,
International Standard Serial Number 1610-3947
505 0# - FORMATTED CONTENTS NOTE
Formatted contents note Support Vector Machines for Classification Problems -- Method of Maximum Margin.-Dual Problem -- Soft Margin -- C- Support Vector Classification.-C- Support Vector Classification with Nominal Attributes -- LOO Bounds for Support Vector Machines.-Introduction -- LOO bounds for ε−Support Vector Regression -- LOO Bounds for Support Vector Ordinal Regression Machine -- Support Vector Machines for Multi-class Classification Problems.-K-class Linear Programming Support Vector Classification Regression Machine (KLPSVCR).-Support Vector Ordinal Regression Machine for Multi-class Problems -- Unsupervised and Semi-Supervised Support Vector Machines -- Unsupervised and Semi-Supervised ν-Support Vector Machine -- Numerical Experiments.-Unsupervised and Semi-supervised Lagrange Support Vector Machine.-Unconstrained Transductive Support Vector Machine.-Robust Support Vector Machines.-Support Vector Ordinal Regression Machine -- Robust Multi-class Algorithm -- Robust Unsupervised and Semi-Supervised Bounded C-Support Vector Machine.-Feature Selection via lp-norm Support Vector Machines.-lp-norm Support Vector Classification.-lp-norm Proximal Support Vector Machine.-Multiple Criteria Linear Programming.-Comparison of Support Vector Machine and Multiple Criteria Programming.-Multiple Criteria Linear Programming.-Multiple Criteria Linear Programming for Multiple Classes -- Penalized Multiple Criteria Linear Programming.-Regularized Multiple Criteria Linear Programs for Classification.-MCLP Extensions -- Fuzzy MCLP.-FMCLP with Soft Constraints.-FMCLP by Tolerances.-Kernel based MCLP -- Knowledge based MCLP -- Rough set based MCLP -- Regression by MCLP.-Multiple Criteria Quadratic Programming.-A General Multiple Mathematical Programming -- Multi-criteria Convex Quadratic Programming Model Kernel based MCQP -- Non-additiveMCLP.-Non-additiveMeasures and Integrals.-Non-additive Classification Models.-Non-additive MCP -- Reducing the time complexity.-Hierarchical Choquet integral.-Choquet integral with respect to k-additive measure.-MC2LP.-MC2LP Classification.-Minimal Error and Maximal Between-class Variance Model.-Firm Financial Analysis.-Finance and Banking -- General Classification Process.-Firm Bankruptcy Prediction -- Personal Credit Management -- Credit Card Accounts Classification -- Two-class Analysis.-FMCLP Analysis -- Three-class Analysis -- Four-class Analysis.-Empirical Study and Managerial Significance of Four-class Models -- Health Insurance Fraud Detection -- Problem Identification -- A Real-life Data Mining Study -- Network Intrusion Detection -- Problem and Two Datasets -- Classify NeWT Lab Data by MCMP, MCMP with kernel and See5 -- Classify KDDCUP-Data by Nine Different Methods -- Internet Service Analysis -- VIP Mail Dataset -- Empirical Study of Cross-validation.-Comparison of Multiple-Criteria Programming Models and SVM.-HIV-1 Informatics -- HIV-1 Mediated Neuronal Dendritic and Synaptic Damage -- Materials and Methods -- Designs of Classifications -- Analytic Results -- Anti-gen and Anti-body Informatics -- Problem Background -- MCQP,LDA and DT Analyses.-Kernel-based MCQP and SVM Analyses.-Geol-chemical Analyses.-Problem Description -- Multiple-class Analyses -- More Advanced Analyses.-Intelligent Knowledge Management -- Purposes of the Study -- Definitions and Theoretical Framework of Intelligent Knowledge.-Some Research Directions.
520 ## - SUMMARY, ETC.
Summary, etc Optimization techniques have been widely adopted to implement various data mining algorithms. In addition to well-known Support Vector Machines (SVMs) (which are based on quadratic programming), different versions of Multiple Criteria Programming (MCP) have been extensively used in data separations. Since optimization based data mining methods differ from statistics, decision tree induction, and neural networks, their theoretical inspiration has attracted many researchers who are interested in algorithm development of data mining. Optimization based Data Mining: Theory and Applications, mainly focuses on MCP and SVM especially their recent theoretical progress and real-life applications in various fields. These include finance, web services, bio-informatics and petroleum engineering, which has triggered the interest of practitioners who look for new methods to improve the results of data mining for knowledge discovery. Most of the material in this book is directly from the research and application activities that the authors’ research group has conducted over the last ten years. Aimed at practitioners and graduates who have a fundamental knowledge in data mining, it demonstrates the basic concepts and foundations on how to use optimization techniques to deal with data mining problems.
650 #0 - SUBJECT ADDED ENTRY--TOPICAL TERM
Topical term or geographic name as entry element Computer science.
650 #0 - SUBJECT ADDED ENTRY--TOPICAL TERM
Topical term or geographic name as entry element Data transmission systems.
650 #0 - SUBJECT ADDED ENTRY--TOPICAL TERM
Topical term or geographic name as entry element Data mining.
650 14 - SUBJECT ADDED ENTRY--TOPICAL TERM
Topical term or geographic name as entry element Computer Science.
650 24 - SUBJECT ADDED ENTRY--TOPICAL TERM
Topical term or geographic name as entry element Data Mining and Knowledge Discovery.
650 24 - SUBJECT ADDED ENTRY--TOPICAL TERM
Topical term or geographic name as entry element Input/Output and Data Communications.
700 1# - ADDED ENTRY--PERSONAL NAME
Personal name Tian, Yingjie.
Relator term author.
700 1# - ADDED ENTRY--PERSONAL NAME
Personal name Kou, Gang.
Relator term author.
700 1# - ADDED ENTRY--PERSONAL NAME
Personal name Peng, Yi.
Relator term author.
700 1# - ADDED ENTRY--PERSONAL NAME
Personal name Li, Jianping.
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
Display text Printed edition:
International Standard Book Number 9780857295033
830 #0 - SERIES ADDED ENTRY--UNIFORM TITLE
Uniform title Advanced Information and Knowledge Processing,
-- 1610-3947
856 40 - ELECTRONIC LOCATION AND ACCESS
Uniform Resource Identifier <a href="http://dx.doi.org/10.1007/978-0-85729-504-0">http://dx.doi.org/10.1007/978-0-85729-504-0</a>
912 ## -
-- ZDB-2-SCS
Holdings
Withdrawn status Lost status Source of classification or shelving scheme Damaged status Not for loan Home library Current library Date acquired Source of acquisition Total Checkouts Barcode Date last seen Price effective from Koha item type
    Dewey Decimal Classification     Central Library Central Library 28/06/2017 Springer EBook   E-38296 28/06/2017 28/06/2017 E-Book

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