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Comparison of SVM and LS-SVM for Regression
2005 International Conference on Neural Networks and Brain, 2006Support vector machines (SVM) has been widely used in classification and nonlinear function estimation. However, the major drawback of SVM is its higher computational burden for the constrained optimization programming. This disadvantage has been overcome by least squares support vector machines (LS-SVM), which solves linear equations instead of a ...
null Haifeng Wang, null Dejin Hu
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2010
Support Vector Machines (SVM) is widely considered to be the best algorithm for text classification because it is based on a well-founded theory (SRM): in the separable case it provides the best result possible for a given set of separation functions, and therefore it does not require tuning.
Jean Beney, Cornelis H. A. Koster
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Support Vector Machines (SVM) is widely considered to be the best algorithm for text classification because it is based on a well-founded theory (SRM): in the separable case it provides the best result possible for a given set of separation functions, and therefore it does not require tuning.
Jean Beney, Cornelis H. A. Koster
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NFL-SVM: An Effective Algorithm for Solving Misclassification in SVM
Third International Conference on Natural Computation (ICNC 2007), 2007Considering the problem of low classification accuracy of similar category, this paper proposes NFL- SVM, an effective algorithm for solving misclassification in SVM. By analysis and deduction, this paper indicates that SVM can be considered the nearest neighbor classifier of just one feature point in each category.
Xianfei Zhang, Bicheng Li, Panyuan
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Optimization of SVM MultiClass by Particle Swarm (PSO-SVM)
2010 2nd International Workshop on Database Technology and Applications, 2010In many problems of classification, the performances of a classifier are often evaluated by a factor (rate of error).the factor is not well adapted for the complex real problems, in particular the problems multiclass. Our contribution consists in adapting an evolutionary method for optimization of this factor.
Fatima Ardjani +2 more
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SVM and Ensemble-SVM in EEG-Based Person Identification
2020Biometric person identification is getting more effective and popular because of Electroencephalography (EEG). EEG signals can be captured from human scalp invasively or non-invasively with the help of electrodes. EEG-based biometric system is more secure and unique for person identification.
Banee Bandana Das +5 more
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Science in China Series G, 2004
Support vector machine (SVM), developed by Vapnik et al., is a new and promising technique for classification and regression and has been proved to be competitive with the best available learning machines in many applications. However, the classification speed of SVM is substantially slower than that of other techniques with similar generalization ...
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Support vector machine (SVM), developed by Vapnik et al., is a new and promising technique for classification and regression and has been proved to be competitive with the best available learning machines in many applications. However, the classification speed of SVM is substantially slower than that of other techniques with similar generalization ...
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RV-SVM: An Efficient Method for Learning Ranking SVM
2009Learning ranking (or preference) functions has become an important data mining task in recent years, as various applications have been found in information retrieval. Among rank learning methods, ranking SVM has been favorably applied to various applications, e.g., optimizing search engines, improving data retrieval quality.
Hwanjo Y, Youngdae K, Seungwon H.
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z-SVM: An SVM for Improved Classification of Imbalanced Data
2006Recent literature has revealed that the decision boundary of a Support Vector Machine (SVM) classifier skews towards the minority class for imbalanced data, resulting in high misclassification rate for minority samples. In this paper, we present a novel strategy for SVM in class imbalanced scenario.
Tasadduq Imam +2 more
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Proceedings of the 23rd international conference on Machine learning - ICML '06, 2006
We extend Support Vector Machines to input spaces that are sets by ensuring that the classifier is invariant to permutations of sub-elements within each input. Such permutations include reordering of scalars in an input vector, re-orderings of tuples in an input matrix or re-orderings of general objects (in Hilbert spaces) within a set as well.
Pannagadatta K. Shivaswamy, Tony Jebara
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We extend Support Vector Machines to input spaces that are sets by ensuring that the classifier is invariant to permutations of sub-elements within each input. Such permutations include reordering of scalars in an input vector, re-orderings of tuples in an input matrix or re-orderings of general objects (in Hilbert spaces) within a set as well.
Pannagadatta K. Shivaswamy, Tony Jebara
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Study of EEG based on SVM and SVM with EMD
Journal of Convergence Information Technology, 2012Abstract Study electroencephalograph (EEG) of epileptic patients during different periods in order to do effective treatments. This paper studies wave forms and energy characteristics of the paroxysmal stage and the static epileptic EEG, adopts two different methods to classify.
Xinxin Wang -, Jianlin Zhao -
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