A randomized algorithm for the exact solution of transductive support vector machines
Random sampling is an efficient method for dealing with constrained optimization problems. In computational geometry, this method has been successfully applied, through Clarkson’s algorithm (Clarkson 1996), to solve a general class of problems called violator spaces. In machine learning, Transductiv...
| Autores: | , |
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| Tipo de recurso: | artículo |
| Fecha de publicación: | 2015 |
| 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:2117/83017 |
| Acceso en línea: | https://hdl.handle.net/2117/83017 https://dx.doi.org/10.1080/08839514.2015.1035951 |
| Access Level: | acceso abierto |
| Palabra clave: | Supervised learning (Machine learning) Semisupervised Learning Transduction Support Vector Machine Classification Aprenentatge automàtic Àrees temàtiques de la UPC::Informàtica::Intel·ligència artificial |
| Sumario: | Random sampling is an efficient method for dealing with constrained optimization problems. In computational geometry, this method has been successfully applied, through Clarkson’s algorithm (Clarkson 1996), to solve a general class of problems called violator spaces. In machine learning, Transductive Support Vector Machines (TSVM) is a learning method used when only a small fraction of labeled data is available, which implies solving a nonconvex optimization problem. Several approximation methods have been proposed to solve it, but they usually find suboptimal solutions. However, a global optimal solution may be obtained by using exact techniques, but at the cost of suffering an exponential time complexity with respect to the number of instances. In this article, an interpretation of TSVM in terms of violator space is given. A randomized method is presented that extends the use of exact methods, thus reducing the time complexity exponentially w.r.t. the number of support vectors of the optimal solution instead of exponentially w.r.t. the number of instances. |
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