Results 291 to 300 of about 442,254 (313)

Comparison of SVM and LS-SVM for Regression

2005 International Conference on Neural Networks and Brain, 2006
Support 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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An incremental LS-SVM learning algorithm ILS-SVM

2011 International Conference on E-Business and E-Government (ICEE), 2011
Least Square Support Vector Machines (in short LS-SVM) reduces the complexity of standard SVM to O(n2). Both SVM and LS-SVM are not suitable for the large scale regression problem. This paper proposes a modifies LS-SVM based on increment datasets, all samples' knowledge is accumulated and some samples is discarded effectively in the incremental ...
Mu Xin-guo, Hao Wen-ning, Zhao En-lai, Chen Gang   +3 more
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SVM Paradoxes

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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Sal/Svm

Virtual Machines and Intermediate Languages, 2010
Presented is the design, implementation and evaluation of a system for computing with non-enumerative set representations. The implementation is in the form of a set assembly language (Sal) whose operations correspond to an implementation of the algebra of sets, with minimal added syntactic sugar; a compiler (Salc) for validation and static ...
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A Comparative study between DPC-SVM and PDPC-SVM

2019 International Conference on Advanced Electrical Engineering (ICAEE), 2019
In this paper we propose a comparative study between Direct Power Control (DPC) and Predictive Direct Power Control (PDPC) with Space Vector Modulation (SVM) for both strategies in a two-level converter applications rectifier PWM (Pulse Width Modulation).
Zakaria El Zair Laggoun, Nebti Khalile, Houcine Benalla   +2 more
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Fusing binary support vector machines (SVM) into multiclass SVM

SPIE Proceedings, 2006
Multi-class support vector machine by fusing a class of binary support vector machines is proposed. The classifier fusion approaches include simple combination method such as Maximum, Minimum, Product, Mean, Median and Major Voting. Dempster-Shafer fusion method is also presented as well as KNN and Neural network approaches.
Zilu Ying, Jingwen Li, Youwei Zhang
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Soft sensor technique using LS-SVM and standard SVM

2005 IEEE International Conference on Information Acquisition, 2006
Support vector machine (SVM) is a modern machine learning method based on Vapnik's statistical learning theory. In this paper, regression support vector machine has been proposed as a tool to soft sensor technique, in which SVM is used to estimate variable which is highly nonlinear.
null Hao-ran Zhang, null Xiao-dong Wang, null Chang-jiang Zhang, null Xiu-ling Xu   +3 more
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SVM and Ensemble-SVM in EEG-Based Person Identification

2020
Biometric 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, Saswat Kumar Ram, Bibudhendu Pati, Chhabi Rani Panigrahi, Korra Sathya Babu, Ramesh Kumar Mohapatra   +5 more
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???? ?????????????????????????????? ?????????????????????????? ???????????????? ???????????? ?????????????? ???????????????? (SVM)

2013
???????????????????? ???????????????????????? ?????????????????????? SVM-???????????? ?????????????? ????????????????, ?????????????????? ?????????????? ?????????????? ?????????????? ???????????????? ??????????????. ???????????? ???????????????????? ?????????????????????????? ???? ???????????? ?????????????????????? ???????????????? ????????????????????
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RV-SVM: An Efficient Method for Learning Ranking SVM

2009
Learning 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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