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UFJF-MLTK: a framework for machine learning algorithms

Marim, Mateus Coutinho ; de Oliveira, Alessandreia Marta ; Villela, Saulo Moraes

Proceedings of the XV Brazilian Symposium on Information Systems, 2019, p.1-8

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Título:
UFJF-MLTK: a framework for machine learning algorithms
Autor: Marim, Mateus Coutinho ; de Oliveira, Alessandreia Marta ; Villela, Saulo Moraes
Assuntos: Computing methodologies -- Machine learning -- Machine learning algorithms ; Software and its engineering -- Software notations and tools -- Development frameworks and environments -- Object oriented frameworks
É parte de: Proceedings of the XV Brazilian Symposium on Information Systems, 2019, p.1-8
Descrição: Machine learning techniques have become increasingly common due to the extension of their application domains and because they can improve their performance when exposed to new data. Several methods have been proposed to address problems of the area, bringing the challenge of comparing different methods to find the one that best solves a problem. Frameworks and libraries focused on learning algorithms can reduce this effort. This paper describes the UFJF-MLTK, an object-oriented framework that helps to choose between different methods, in the development of new algorithms through the instantiation of a C++ class architecture that covers various types of learning algorithms and also helps in teaching the subject. We discuss the problems faced in the project architecture, the components of the framework, the algorithms that currently compose it, how it was documented and examples of its instantiation.
Editor: New York, NY, USA: ACM
Idioma: Inglês

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