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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References
Boyd, S., Vandenberghe, L.: Convex Optimization. Cambridge University Press (2004)
Chang, C.-C., Lin, C.-J.: Libsvm: a library for support vector machines. ACM Transactions on Intelligent Systems and Technology 2(3) (2011)
Chapelle, O., Vapnik, V.N.: Bounds on error expectation for support vector machines. Neural Computation 12(9), 2012–2036 (2000)
Chapelle, O., Vapnik, V.N.: Choosing multiple parameters for support vector machines. Machine Learning 46(1-3), 131–159 (2001)
Cortes, C., Vapnik, V.N.: Support-vector networks. Machine Learning 20(3), 273–297 (1995)
Fan, R.-E., Chen, P.-H., Lin, C.-J.: Working set selection using second order information for training support vector machines. The Journal of Machine Learning Research 6, 1889–1918 (2005)
Frank, R.: The perceptron a perceiving and recognizing automaton. Technical Report 85-460-1, Cornell Aeronautical Laboratory (1957)
Fung, G., Mangasarian, O.L.: Proximal support vector machine classifiers. In: Proceedings of the Seventh ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, San Francisco, CA, USA, pp. 77–86. ACM, New York (2001)
Garg, A., Har-Peled, S., Roth, D.: On generalization bounds, projection profile, and margin distribution. In: ICML 2002 Proceedings of the Nineteenth International Conference on Machine Learning, Sydney, Australia, pp. 171–178. Morgan Kaufmann Publishers Inc. (2002)
Garg, A., Roth, D.: Margin distribution optimization. Computational Imaging and Vision 29, 119–128 (2005)
Jayadeva, Khemchandani, R., Chandra, S.: Twin support vector machines for pattern classification. IEEE Transactions on Pattern Analysis and Machine Intelligence 29(5), 905–910 (2007)
Joachims, T., Ndellec, C., Rouveriol, C.: Text categorization with support vector machines: learning with many relevant features. In: Nédellec, C., Rouveirol, C. (eds.) ECML 1998. LNCS, vol. 1398, pp. 137–142. Springer, Heidelberg (1998)
Kotsiantis, S.B.: Supervised machine learning: A review of classification techniques. Informatica 31, 3–24 (2007)
Kumar, A.M., Gopal, M.: Least squares twin support vector machines for pattern classification. Expert Systems with Applications: An International Journal 36(4), 7535–7543 (2009)
Lee, S., Verri, A.: SVM 2002. LNCS, vol. 2388. Springer, Heidelberg (2002)
Mangasarian, O.L., Wild, W.: Multisurface proximal support vector machine classification via generalized eigenvalues. IEEE Transaction on Pattern Analysis and Machine Intelligence 28(1), 69–74 (2005)
Noble, W.S.: Support vector machine applications in computational biology. In: Kernel Methods in Computational Biology, pp. 71–92 (2004)
Qi, Z., Tian, Y., Shi, Y.: Robust twin support vector machine for pattern classification. Pattern Recognition (June 27, 2012) (accepted)
Vapnik, V.N., Boser, B.E., Guyon, I.: A training algorithm for optimal margin classifiers. In: COLT 1992 Proceedings of the Fifth Annual Workshop on Computational Learning, Pittsburgh, PA, USA, vol. 5, pp. 144–152. ACM, New York (1992)
Vapnik, V.N.: Statistical learning theory. John Wiley and Sons Inc. (1998)
Witten, I.H., Frank, E., Hall, M.A.: Data Mining: Practical Machine Learning Tools and Techniques. Morgan Kaufmann (2011)
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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
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