Abstract
A binary classification algorithm, called Probabilistic Vector Machine – PVM, is proposed. It is based on statistical measurements of the training data, providing a robust and lightweight classification model with reliable performance. The proposed model is also shown to provide the optimal binary classifier, in terms of probability of error, under a set of loose conditions regarding the data distribution. We compare PVM against GEPSVM and PSVM and provide evidence of superior performance on a number of datasets in terms of average accuracy and standard deviation of accuracy.
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Cimpoeşu, M., Sucilă, A., Luchian, H. (2013). A Statistical Binary Classifier: Probabilistic Vector Machine. In: Correia, L., Reis, L.P., Cascalho, J. (eds) Progress in Artificial Intelligence. EPIA 2013. Lecture Notes in Computer Science(), vol 8154. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-40669-0_19
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DOI: https://doi.org/10.1007/978-3-642-40669-0_19
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-40668-3
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