Improving sparsity in online kernel models
In this thesis, background theory about the online kernel-based algorithms and their use for online learning is presented. The analysis of the state-ofthe- art methods highlights an important drawback in many kernel online learning algorithms. This is the large memory storage needed due to the amoun...
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| Tipo de recurso: | tesis de maestría |
| Fecha de publicación: | 2013 |
| País: | España |
| Institución: | Universitat Politècnica de Catalunya (UPC) |
| Repositorio: | UPCommons. Portal del coneixement obert de la UPC |
| Idioma: | inglés |
| OAI Identifier: | oai:upcommons.upc.edu:2099.1/20440 |
| Acceso en línea: | https://hdl.handle.net/2099.1/20440 |
| Access Level: | acceso abierto |
| Palabra clave: | Kernel functions Machine learning Kernel, Funcions de Aprenentatge automàtic Àrees temàtiques de la UPC::Informàtica::Intel·ligència artificial::Aprenentatge automàtic |
| Sumario: | In this thesis, background theory about the online kernel-based algorithms and their use for online learning is presented. The analysis of the state-ofthe- art methods highlights an important drawback in many kernel online learning algorithms. This is the large memory storage needed due to the amount of support vectors generated. We study the SCA approach for reducing support vectors in the batch learning case and propose its adaptation to the online scenario. POLSCA is the algorithm proposed for solving the addressed problems that online learning presents. The proposed algorithm is constructed by merging the concepts of Primal formulation of the optimization problem, online learning with stochastic subgradient descent solver(PEGASOS) and the support vector reduction method SCA. |
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