The project’s goal is to apply advanced Machine Learning methodologies, mainly based on SVMs, and the related effective numerical methods for the solution of the underlying optimization problem. The analysis aims to identify the most suitable solution strategies for a given application context, or for a specific problem instance, by exploiting the characteristics of its mathematical model. In addition to the binary classification, also multiclass and (nonlinear) regression problems can be considered. The algorithmic study is followed by a code prototyping, with the possible development of a scalar or high-performance software, tailored to apply the studied strategies to the particular needs for the user. The research group makes its skills on Numerical Analysis, Numerical Optimization, scalar and concurrent programming available to the project.

Learning from examples: methodologies and software / Bonettini, Silvia; Prato, Marco; Ruggiero, V.; Zanella, R.; Zanghirati, G.; Zanni, Luca. - STAMPA. - (2011).

Learning from examples: methodologies and software

BONETTINI, Silvia;PRATO, Marco;ZANNI, Luca
2011

Abstract

The project’s goal is to apply advanced Machine Learning methodologies, mainly based on SVMs, and the related effective numerical methods for the solution of the underlying optimization problem. The analysis aims to identify the most suitable solution strategies for a given application context, or for a specific problem instance, by exploiting the characteristics of its mathematical model. In addition to the binary classification, also multiclass and (nonlinear) regression problems can be considered. The algorithmic study is followed by a code prototyping, with the possible development of a scalar or high-performance software, tailored to apply the studied strategies to the particular needs for the user. The research group makes its skills on Numerical Analysis, Numerical Optimization, scalar and concurrent programming available to the project.
2011
18-20 maggio 2011
Bologna
Bonettini, Silvia; Prato, Marco; Ruggiero, V.; Zanella, R.; Zanghirati, G.; Zanni, Luca
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11380/797289
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