Transductive Support Vector Machines and Applications in Bioinformatics for Promoter Recognition

dc.contributor.authorKasabov, N
dc.contributor.authorPang, S.
dc.date.accessioned2009-05-27T22:18:53Z
dc.date.available2009-05-27T22:18:53Z
dc.date.copyright2004
dc.date.created2004
dc.date.issued2004
dc.description.abstractThis paper introduces a novel transductive support vector machine (TSVM) model and compares it with the traditional inductive SVM on a key problem in bioinformatics - promoter recognition. While inductive reasoning is concerned with the development of a model (a function) to approximate data from the whole problem space (induction), and consecutively using this model to predict output values for a new input vector (deduction), in the transductive inference systems a model is developed for every new input vector based on some closest to the new vector data from an existing database and this model is used to predict only the output for this vector. The TSVM outperforms by far the inductive SVM models applied on the same problems. Analysis is given on the advantages and disadvantages of the TSVM. Hybrid TSVM-evolving connections systems are discussed as directions for future research.
dc.identifier.doi10.1109/ICNNSP.2003.1279199
dc.identifier.urihttps://hdl.handle.net/10292/607
dc.publisherIEEE
dc.rights©2004 IEEE. Personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component of this work in other works must be obtained from the IEEE.
dc.rights.accessrightsOpenAccess
dc.sourceNeural Information Processing - Letters and Reviews, 3, 2, 31-38
dc.titleTransductive Support Vector Machines and Applications in Bioinformatics for Promoter Recognition
dc.typeJournal Article
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