000 06455nam a22005175i 4500
001 978-0-85729-504-0
003 DE-He213
005 20170628033423.0
007 cr nn 008mamaa
008 110516s2011 xxk| s |||| 0|eng d
020 _a9780857295040
_9978-0-85729-504-0
024 7 _a10.1007/978-0-85729-504-0
_2doi
050 4 _aQA76.9.D343
072 7 _aUNF
_2bicssc
072 7 _aUYQE
_2bicssc
072 7 _aCOM021030
_2bisacsh
082 0 4 _a006.312
_223
100 1 _aShi, Yong.
_eauthor.
245 1 0 _aOptimization Based Data Mining: Theory and Applications
_h[electronic resource] /
_cby Yong Shi, Yingjie Tian, Gang Kou, Yi Peng, Jianping Li.
264 1 _aLondon :
_bSpringer London,
_c2011.
300 _aXVI, 316 p.
_bonline resource.
336 _atext
_btxt
_2rdacontent
337 _acomputer
_bc
_2rdamedia
338 _aonline resource
_bcr
_2rdacarrier
347 _atext file
_bPDF
_2rda
490 1 _aAdvanced Information and Knowledge Processing,
_x1610-3947
505 0 _aSupport 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 _aOptimization 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 _aComputer science.
650 0 _aData transmission systems.
650 0 _aData mining.
650 1 4 _aComputer Science.
650 2 4 _aData Mining and Knowledge Discovery.
650 2 4 _aInput/Output and Data Communications.
700 1 _aTian, Yingjie.
_eauthor.
700 1 _aKou, Gang.
_eauthor.
700 1 _aPeng, Yi.
_eauthor.
700 1 _aLi, Jianping.
_eauthor.
710 2 _aSpringerLink (Online service)
773 0 _tSpringer eBooks
776 0 8 _iPrinted edition:
_z9780857295033
830 0 _aAdvanced Information and Knowledge Processing,
_x1610-3947
856 4 0 _uhttp://dx.doi.org/10.1007/978-0-85729-504-0
912 _aZDB-2-SCS
999 _c15117
_d15117