Results 251 to 260 of about 7,794,647 (291)

Robust Support Vector Machine

2014 International Joint Conference on Neural Networks (IJCNN), 2014
Support Vector Machine (SVM) is a well-known kernel-based method for binary classification problem. SVM aims at constructing the optimal middle hyperplane which induces the largest margin. It is proven that in a linearly separable case, this middle hyperplane offers the high accuracy on universal datasets.
Trung Le 0001, Dat Tran 0001, Wanli Ma 0003, Thien Pham, Phuong Duong, Minh Nguyen 0004   +5 more
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Distributed Support Vector Machines

IEEE Transactions on Neural Networks, 2006
A truly distributed (as opposed to parallelized) support vector machine (SVM) algorithm is presented. Training data are assumed to come from the same distribution and are locally stored in a number of different locations with processing capabilities (nodes).
Ángel Navia-Vázquez, D. Gutiérrez-González, Emilio Parrado-Hernández, J. J. Navarro-Abellan   +3 more
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Hierarchical support vector machines

Proceedings. 2005 IEEE International Geoscience and Remote Sensing Symposium, 2005. IGARSS '05., 2005
The speed and accuracy of a hierarchical SVM (H-SVM) depend on its tree structure. To achieve high performance, more separable classes should be separated at the upper nodes of a decision tree. Because SVM separates classes at feature space determined by the kernel function, separability in feature space should be considered.
Zhigang Liu 0012, Wenzhong Shi, Qianqing Qin, Xiaowen Li 0001, Donghui Xie   +4 more
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Support vector machines

ACM SIGKDD Explorations Newsletter, 2000
Support Vector Machines (SVMs) and related kernel methods have become increasingly popular tools for data mining tasks such as classification, regression, and novelty detection. The goal of this tutorial is to provide an intuitive explanation of SVMs from a geometric perspective.
Kristin P. Bennett, Colin Campbell
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A neural support vector machine

Neural Networks, 2010
Support vector machines are state-of-the-art pattern recognition algorithms that are well founded in optimization and generalization theory but not obviously applicable to the brain. This paper presents Bio-SVM, a biologically feasible support vector machine.
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Selective support vector machines

Journal of Combinatorial Optimization, 2008
zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Onur Seref, O. Erhun Kundakcioglu, Oleg A. Prokopyev, Panos M. Pardalos   +3 more
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Normalization in Support Vector Machines

2001
This article deals with various aspects of normalization in the context of Support Vector Machines. We consider fist normalization of the vectors in the input space and point out the inherent limitations. A natural extension to the feature space is then represented by the kernel function normalization.
Graf, A., Borer, S.
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Support vector machines

American Journal of Orthodontics and Dentofacial Orthopedics, 2023
Dirk Valkenborg, Axel-Jan Rousseau, Melvin Geubbelmans, Tomasz Burzykowski   +3 more
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Structural Support Vector Machine

2008
Support Vector Machine (SVM) is one of the most popular classifiers in pattern recognition, which aims to find a hyperplane that can separate two classes of samples with the maximal margin. As a result, traditional SVM usually more focuses on the scatter between classes, but neglects the different data distributions within classes which are also vital ...
Hui Xue 0002, Songcan Chen, Qiang Yang 0001   +2 more
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An Integer Support Vector Machine

Sixth International Conference on Software Engineering, Artificial Intelligence, Networking and Parallel/Distributed Computing and First ACIS International Workshop on Self-Assembling Wireless Networks (SNPD/SAWN'05), 2005
Data mining is a technique to discover patterns and trends in data and can be used to create a model to predict those patterns and trends. This is particularly useful for data sets that are not amenable to traditional statistical analysis. One particular data mining task is classification, predicting a quantity that can only take on a finite number of ...
Maryanne Domm, Andrew Engel, Peguy Pierre-Louis, Jeff Goldberg   +3 more
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