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Support vector machine classifier with truncated pinball loss
Pattern Recognition, 2017A new loss function and corresponding support vector machine are proposed.The model can handle feature noise.The model keeps sparsity to a certain extent.The problem is solved by concave-convex procedure and decomposition method. Feature noise, namely noise on inputs is a long-standing plague to support vector machine(SVM).
Xin Shen 0003 +3 more
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Classifying E-Mails Via Support Vector Machine
2006For addressing the growing problem of junk E-mail on the Internet, this paper proposes an effective E-mail classifying technique. Our work handles E-mail messages as semi-structured documents consisting of a set of fields with predefined semantics and a number of variable length free-text contents.
Lidan Shou +3 more
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Building Support Vector Machines with Reduced Classifier Complexity.
J. Mach. Learn. Res., 2006Support vector machines (SVMs), though accurate, are not preferred in applications requiring great classification speed, due to the number of support vectors being large. To overcome this problem we devise a primal method with the following properties: (1) it decouples the idea of basis functions from the concept of support vectors; (2) it greedily ...
Keerthi, S., Chapelle, O., DeCoste, D.
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Optimal Feature Selection for Support Vector Machine Classifiers
2020 IEEE International Conference on Industrial Engineering and Engineering Management (IEEM), 2020Binary classification is a fundamental task in machine learning. It consists of learning a relationship between observable features of a set of training objects and their observable membership to either of two classes to predict as accurately as possible the class membership of new test objects whose features are observable but whose class membership ...
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A sparse least squares support vector machine classifier
2004 IEEE International Joint Conference on Neural Networks (IEEE Cat. No.04CH37541), 2005Since the early 90's, support vector machines (SVM) are attracting more and more attention due to their applicability to a large number of problems. To overcome the high computational complexity of traditional support vector machines, previously a new technique, the least squares SVM (LS-SVM) has been introduced, but unfortunately a very attractive ...
Valyon, József, Horváth, Gábor
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An Effective Method of Pruning Support Vector Machine Classifiers
IEEE Transactions on Neural Networks, 2010Support vector machine (SVM) classifiers often contain many SVs, which lead to high computational cost at runtime and potential overfitting. In this paper, a practical and effective method of pruning SVM classifiers is systematically developed. The kernel row vectors, with one-to-one correspondence to the SVs, are first organized into clusters.
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Efficient geometric algorithms for support vector machine classifier
2010 Sixth International Conference on Natural Computation, 2010The geometric approaches based on reduced convex hull (RCH) are promising methods for solving support vector machine (SVM), which have been the focus of intense theoretical as well as application-oriented research in machine learning. In this paper, two efficient geometric learning algorithms for SVM, termed as DNP-GA and PDNP-GA, are proposed by ...
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Classifying Consciousness States with Support Vector Machine
Proceedings of the 2024 7th International Conference on Machine Learning and Machine Intelligence (MLMI)Jingming Gong +2 more
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A twin-hypersphere support vector machine classifier and the fast learning algorithm
Information Sciences, 2013Xinjun Peng
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