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A unified framework for Support Vector Machines, Multiple Kernel Learning and metric learning |
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Defense | Thèse de doctorat : Univ. Genève, 2012 - Sc. 4478 - 2012/10/22 | |
Abstract | In this work we studied several families of learning algorithms, including Support Vector Machines, Multiple Kernel Learning and metric learning. We focused on two fundamental tasks in Machine Learning which are classification and feature selection. We derived novel learning algorithms for classification and for feature selection, which outperformed the state-of-the-art in several experiments. Moreover, we discovered the relationship among algorithms and built a unified framework of various learning algorithms, more precisely, a unified framework of Support Vector Machines, Multiple Kernel Learning and metric learning. | |
Keywords | Machine Learning — Artificial Intelligence — Computer Science — Support Vector Machines — Multiple Kernel Learning — Metric Learning — Unified framework — Learning algorithms — Optimization | |
Identifiers | URN: urn:nbn:ch:unige-240049 | |
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Research group | Geneva Artificial Intelligence Laboratory | |
Projects | DROPTOP FP7: DebugIT FP7: e-LICO | |
Citation (ISO format) | DO, Thi Thanh Huyen. A unified framework for Support Vector Machines, Multiple Kernel Learning and metric learning. Université de Genève. Thèse, 2012. doi: 10.13097/archive-ouverte/unige:24004 https://archive-ouverte.unige.ch/unige:24004 |