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2006

Machine learning: a review of classification and combining techniques

8 years 6 months ago
Machine learning: a review of classification and combining techniques
Abstract Supervised classification is one of the tasks most frequently carried out by socalled Intelligent Systems. Thus, a large number of techniques have been developed based on Artificial Intelligence (Logic-based techniques, Perceptron-based techniques) and Statistics (Bayesian Networks, Instance-based techniques). The goal of supervised learning is to build a concise model of the distribution of class labels in terms of predictor features. The resulting classifier is then used to assign class labels to the testing instances where the values of the predictor features are known, but the value of the class label is unknown. This paper describes various classification algorithms and the recent attempt for improving classification accuracy--ensembles of classifiers. Keywords Classifiers
Sotiris B. Kotsiantis, Ioannis D. Zaharakis, Panay
Added 10 Dec 2010
Updated 10 Dec 2010
Type Journal
Year 2006
Where AIR
Authors Sotiris B. Kotsiantis, Ioannis D. Zaharakis, Panayiotis E. Pintelas
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