Results 121 to 130 of about 125,053 (147)

PB-SVM Ensemble: A SVM Ensemble Algorithm Based on SVM

Applied Mechanics and Materials, 2014
As one of the most popular and effective classification algorithms, Support Vector Machine (SVM) has attracted much attention in recent years. Classifiers ensemble is a research direction in machine learning and statistics, it often gives a higher classification accuracy than the single classifier.
Bo Wang, Yu Kai Yao, Xiao Ping Wang, Xiao Yun Chen   +3 more
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Stacked-SVM

Proceedings of the 2019 2nd International Conference on Algorithms, Computing and Artificial Intelligence, 2019
Recent years witnesses the rampancy of telephone fraud along with the development of modern communication technology. The challenges from telephone fraud identification mainly exist in two aspects: (1) the telephone fraud records are typical imbalanced data due to the characteristic of heterogeneous spatial-temporal distribution, leading to bias ...
Qingqing Chang, Shaofu Lin, Xiliang Liu
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SVM optimization

Proceedings of the 25th international conference on Machine learning - ICML '08, 2008
We discuss how the runtime of SVM optimization should decrease as the size of the training data increases. We present theoretical and empirical results demonstrating how a simple subgradient descent approach indeed displays such behavior, at least for linear kernels.
Shai Shalev-Shwartz, Nathan Srebro
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A new type SVM??projected SVM

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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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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