Kernel methods with mixed data types and their applications
Support Vector Machines (SVMs) represent a category of supervised machine learning algorithms that find extensive application in both classification and regression tasks. In these algorithms, kernel functions are responsible for measuring the similarity between input samples to generate models and p...
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| Tipo de recurso: | tesis de maestría |
| Fecha de publicación: | 2023 |
| 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:2117/394523 |
| Acceso en línea: | https://hdl.handle.net/2117/394523 |
| Access Level: | acceso abierto |
| Palabra clave: | Kernel functions Machine learning Support Vector Machines Funcions de Kernel Aprenentatge Automàtic Ciència de Dades Bagging-SVM Aggregation Kernel Kernel Functions Machine Learning Data Science Kernel, Funcions de Aprenentatge automàtic Àrees temàtiques de la UPC::Informàtica::Intel·ligència artificial::Aprenentatge automàtic |
| Sumario: | Support Vector Machines (SVMs) represent a category of supervised machine learning algorithms that find extensive application in both classification and regression tasks. In these algorithms, kernel functions are responsible for measuring the similarity between input samples to generate models and perform predictions. In order for SVMs to tackle data analysis tasks involving mixed data, the implementation of a valid kernel function for this purpose is required. However, in the current literature, we hardly find any kernel function specifically designed to measure similarity between mixed data. In addition, there is a complete lack of significant examples where these kernels have been practically implemented. Another notable characteristic of SVMs is their remarkable efficacy in addressing high-dimensional problems. However, they can become inefficient when dealing with large volumes of data. In this project, we propose the formulation of a kernel function capable of accurately capturing the similarity between samples of mixed data. We also present an SVM algorithm based on Bagging techniques that enables efficient analysis of large volumes of data. Additionally, we implement both proposals in an updated version of the successful SVM library LIBSVM. Moreover, we evaluate their effectiveness, robustness and efficiency, obtaining promising results. |
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