• Produktbild: Optimization Based Data Mining: Theory and Applications
  • Produktbild: Optimization Based Data Mining: Theory and Applications

Optimization Based Data Mining: Theory and Applications

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Beschreibung

Produktdetails

Einband

Taschenbuch

Erscheinungsdatum

12.07.2013

Verlag

Springer London

Seitenzahl

316

Maße (L/B/H)

23.5/15.5/1.9 cm

Gewicht

505 g

Auflage

2011

Sprache

Englisch

ISBN

978-1-4471-2653-9

Beschreibung

Produktdetails

Einband

Taschenbuch

Erscheinungsdatum

12.07.2013

Verlag

Springer London

Seitenzahl

316

Maße (L/B/H)

23.5/15.5/1.9 cm

Gewicht

505 g

Auflage

2011

Sprache

Englisch

ISBN

978-1-4471-2653-9

Herstelleradresse

Springer-Verlag GmbH
Tiergartenstr. 17
69121 Heidelberg
DE

Email: ProductSafety@springernature.com

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  • Produktbild: Optimization Based Data Mining: Theory and Applications
  • Produktbild: Optimization Based Data Mining: Theory and Applications
  • 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.-Choquetintegral 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.