Support Vector Data Description

Chandan Srivastava

ISBN 10: 3844385215 / ISBN 13: 9783844385212

Published by LAP LAMBERT Academic Publishing Jun 2011, 2011

About this Item

This item is printed on demand - Print on Demand Titel. Neuware -This book is based on the basis that machine learningalgorithms and rank based statistical methods are abetter choice to develop a robust model in logicalsituations. We designed experimental setup for datacollection, developed unique class of model includingvariable selection, and detection methods. Theselected significant variables provide a unique classof model for all six participants. We emphasize thebest selected variables have good information formodel development, and each selected variable have noerror i.e.; AUC=1, with forward selection and supportvector data description classifier. Basically, wedeveloped a unique class of model using six differentclasses of subjects, predicting elderly fallprevention, and after doing external validation withseventh class of subject, we reached a uniquesolution. Sections one is research introduction,section two is all about research design and dataanalysis, section three and four give extensivedevelopment of model for variable selection and oneclass classifier. Then finally given the conclusionand future aspect of whole study.VDM Verlag, Dudweiler Landstraße 99, 66123 Saarbrücken 72 pp. Englisch.

Seller Inventory # 9783844385212

Bibliographic Details

• Title: Support Vector Data Description
• Publisher: LAP LAMBERT Academic Publishing Jun 2011
• Publication Date: 2011
• Language: English
• ISBN 10: 3844385215
• ISBN 13: 9783844385212
• Binding: Taschenbuch
• Condition: Neu
• Dimensions: 220W x 150H x 5D millimeters 220 millimeters width by 150 millimeters height by 5 millimeters depth
• Weight: 125 grams

About this title

Synopsis

This book is based on the basis that machine learningalgorithms and rank based statistical methods are abetter choice to develop a robust model in logicalsituations. We designed experimental setup for datacollection, developed unique class of model includingvariable selection, and detection methods. Theselected significant variables provide a unique classof model for all six participants. We emphasize thebest selected variables have good information formodel development, and each selected variable have noerror i.e.; AUC=1, with forward selection and supportvector data description classifier. Basically, wedeveloped a unique class of model using six differentclasses of subjects, predicting elderly fallprevention, and after doing external validation withseventh class of subject, we reached a uniquesolution. Sections one is research introduction,section two is all about research design and dataanalysis, section three and four give extensivedevelopment of model for variable selection and oneclass classifier. Then finally given the conclusionand future aspect of whole study.

About the Author

Chandan Srivastava is doctoral research fellow at Department deEnginyeria Química, Universitat Rovira i Virgili,Spain. He hasobtained his master study and research at the Department ofSystem Optimization and Security, University of Technology ofTroyes,France. His area of research includes machinelearning, and biomedical signal processing.