Selectivity Supervision in Combining Pattern-Recognition Modalities by Feature- and Kernel-Selective Support Vector Machines
Tatarchuk, A, Mottl, V, Eliseyev, A and Windridge, D (2008) Selectivity Supervision in Combining Pattern-Recognition Modalities by Feature- and Kernel-Selective Support Vector Machines In: 19th ICPR 2008, 2008-12-08 - 2008-12-11, Tampa, USA.
Available under License : See the attached licence file.
Multi-modal pattern recognition must frequently truncate the set of initially available modalities. When a kernel-based approach is adopted within each modality, the problem of modality selection becomes mathematically analogous to that of wrapper-based feature selection. In this paper, we revise two implicitly wrapper-based methods of SVM-embedded selective kernel combination, the Relevance and Support Kernel Machines, so as to equip them with the ability to preset the desired level of feature-selectivity. Hence, a continuous axis of nested feature selection models is obtained, ranging from the absence of selectivity to the selection of single features. We thus unite the distinct processes of selection and classification within the two techniques in manner suitable for general application within Kernel-based multi-modal pattern recognition.
|Item Type:||Conference or Workshop Item (Conference Paper)|
|Divisions :||Faculty of Engineering and Physical Sciences > Electronic Engineering > Centre for Vision Speech and Signal Processing|
|Identification Number :||10.1109/ICPR.2008.4761781|
|Additional Information :||© 2008 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.|
|Depositing User :||Symplectic Elements|
|Date Deposited :||18 Sep 2013 12:45|
|Last Modified :||23 Sep 2013 20:17|
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