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Type: Artigo de periódico
Author: Rocha, A
Papa, JP
Meira, LAA
Abstract: With several good research groups actively working in machine learning (ML) approaches, we have now the concept of self-containing machine learning solutions that oftentimes work out-of-the-box leading to the concept of ML black-boxes. Although it is important to have such black-boxes helping researchers to deal with several problems nowadays, it comes with an inherent problem increasingly more evident: we have observed that researchers and students are progressively relying on ML black-boxes and, usually, achieving results without knowing the machinery of the classifiers. In this regard, this paper discusses the use of machine learning black-boxes and poses the question of how far we can get using these out-of-the-box solutions instead of going deeper into the machinery of the classifiers. The paper focuses on three aspects of classifiers: (1) the way they compare examples in the feature space; (2) the impact of using features with variable dimensionality; and (3) the impact of using binary classifiers to solve a multi-class problem. We show how knowledge about the classifier's machinery can improve the results way beyond out-of-the-box machine learning solutions.
Subject: Machine learning black-boxes
binary to multi-class classifiers
support vector machines
optimum-path forest
visual words
K-nearest neighbors
Country: Singapura
Editor: World Scientific Publ Co Pte Ltd
Citation: International Journal Of Pattern Recognition And Artificial Intelligence. World Scientific Publ Co Pte Ltd, v. 26, n. 2, 2012.
Rights: fechado
Identifier DOI: 10.1142/S0218001412610010
Date Issue: 2012
Appears in Collections:Unicamp - Artigos e Outros Documentos

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