Making kernel machines scalable combining matrix approximations and distributed computing
In this work, kernelized binary support vector machines are implemented based on stochastic gradient descent. The Scala library can be used both on a single computing node and on a Spark cluster. Additional tools for parameter tuning, subset selection, and model evaluation are implemented.
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| Formato: | tesis de maestría |
| Fecha de publicación: | 2018 |
| País: | España |
| Recursos: | Universitat Politècnica de Catalunya (UPC) |
| Repositorio: | UPCommons. Portal del coneixement obert de la UPC |
| Idioma: | inglés |
| OAI Identifier: | oai:upcommons.upc.edu:2117/118760 |
| Acesso em linha: | https://hdl.handle.net/2117/118760 |
| Access Level: | acceso abierto |
| Palavra-chave: | Kernel functions Support vector machines Machine learning SVM support vector machine kernel methods map reduce Apache Spark Kernel, Funcions de Aprenentatge automàtic Àrees temàtiques de la UPC::Informàtica |
| Resumo: | In this work, kernelized binary support vector machines are implemented based on stochastic gradient descent. The Scala library can be used both on a single computing node and on a Spark cluster. Additional tools for parameter tuning, subset selection, and model evaluation are implemented. |
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