Probabilistic classiﬁers are among the most popular classiﬁcation methods adopted by the machine learning community. They are often based on a-priori knowledge about the probability distribution underlying the data; nevertheless this information is rarely provided, so that a family of probability distribution functions is assumed to be an approximation model. In this paper we present an efﬁcient binary classiﬁ cation algorithm, called Perceptron-IPCAC (PIPCAC), assuming that each class is distributed accordingly to a Mixture of Gaussian functions. PIPCAC is deﬁned as a multilayer perceptron trained by combining different linear classiﬁers. The algorithm has been tested on both synthetic and real datasets, and the obtained results demonstrate the effectiveness and efﬁciency of the proposed method. Furthermore, the promising performances have been conﬁrmed by the comparison of its results with those achieved by Support Vector Machines.
|Titolo:||PIPCAC: A Novel Binary Classifier Assuming Mixtures of Gaussian Functions|
|Parole Chiave:||Machine Learning ; Neural Networks ; Binary Classiﬁcation Algorithm ; Mixtures of Gaussian Functions|
|Settore Scientifico Disciplinare:||Settore INF/01 - Informatica|
|Data di pubblicazione:||2010|
|Tipologia:||Book Part (author)|
|Appare nelle tipologie:||03 - Contributo in volume|