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Proceedings of the 1st International Conference on Internet of Things and Machine Learning, 2017
Conventional classifiers often regard input samples as identically and independently distributed (i.i.d.). This is however not true in many real applications, especially when patterns occur as groups (where each group shares a homogeneous style). Such tasks are also called field classification. By breaking the i.i.d.
Kaizhu Huang +2 more
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Conventional classifiers often regard input samples as identically and independently distributed (i.i.d.). This is however not true in many real applications, especially when patterns occur as groups (where each group shares a homogeneous style). Such tasks are also called field classification. By breaking the i.i.d.
Kaizhu Huang +2 more
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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 +5 more
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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 +5 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), 2005Data 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 +3 more
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Possibilistic support vector machines
Pattern Recognition, 2005zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Ki Young Lee +3 more
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Selective support vector machines
Journal of Combinatorial Optimization, 2008zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Onur Seref +3 more
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Distributed Support Vector Machines
IEEE Transactions on Neural Networks, 2006A 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 +3 more
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Normalization in Support Vector Machines
2001This 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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Hierarchical support vector machines
Proceedings. 2005 IEEE International Geoscience and Remote Sensing Symposium, 2005. IGARSS '05., 2005The 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 +4 more
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Optimisation on support vector machines
Proceedings of the IEEE-INNS-ENNS International Joint Conference on Neural Networks. IJCNN 2000. Neural Computing: New Challenges and Perspectives for the New Millennium, 2000We deal with the optimisation problem involved in determining the maximal margin separation hyperplane in support vector machines. We consider three different formulations, based on L/sub 2/ norm distance (the standard case), L/sub 1/ norm, and L/sub /spl infin// norm.
João Pedro Pedroso, Noboru Murata
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A neural support vector machine
Neural Networks, 2010Support 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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