[PDF] Top 20 Support vector machines for data classification
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Support vector machines for data classification
... • Engine misfire: a substantial fraction of a cylinder’s air-fuel mixture fails to ignite.. • Frequent misfires: pollutants and costly replacement[r] ... See full document
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Support vector machines for data classification and regression
... Avoid underfitting : small training error Avoid overfitting : small testing error.. Overfitting[r] ... See full document
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Expediting Model Selection for Support Vector Machines Based on Data Reduction
... years, Support Vector Machines (SVM) have been extensively applied to deal with various data classification ...selection for SVM. This issue is of par- ticular significant ... See full document
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Training algorithms for fuzzy support vector machines with noisy data
... This equips FSVMs with the ability to train data with noises or outliers by setting lower fuzzy memberships to the data points that are considered as noises or outliers with highe[r] ... See full document
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Decomposition methods for linear support vector machines
... (Due to space limit, we discuss the difference of their implementations in [Z].) Thus, it is very difficult to conduct a fair comparison. Our goal here is only to demonstrate.. tha[r] ... See full document
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Reduction Techniques for Training Support Vector Machines
... • Accuracy: all RSVM implementations lower than SVM • LS-SVM a little lower among RSVM implementations • Optimal models for RSVM have much larger C. • For median-sized problems RSVM not [r] ... See full document
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Decomposition Methods for Linear Support Vector Machines
... 2 Drawbacks of Decomposition Methods for Lin- ear SVMs with n ¿ l The decomposition method is an iterative procedure. In each iteration, the index set of variables is separated to two sets B and N, where B is the ... See full document
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Reduction techniques for training support vector machines
... implementations for RSVM and compared them with two decomposition methods based on ...restricting support vectors from a randomly selected subset still downgrades the ...strategy. For the training ... See full document
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LIBSVM: a Library for Support Vector Machines
... C-support vector classification (C-SVC), ν-support vector classification (ν-SVC), distribution estimation (one-class SVM), -support vector regression (-SVR), and ... See full document
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A Simple Decomposition Method for Support Vector Machines
... iterations. For most problems we have tested, not only those presented here, we observe slow convergence of decomposition methods when C is ...of support and bounded support vectors are decreased ... See full document
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Credit scoring with a data mining approach based on support vector machines
... credit data are collected by the credit department of the ...the support of the credit classification model, the manager can accurately evaluate the applicant’s credit ...score. Support Vector ... See full document
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The analysis of decomposition methods for support vector machines
... Methods for Support Vector Machines Chih-Chung Chang, Chih-Wei Hsu, and Chih-Jen Lin Abstract—The support vector machine (SVM) is a new and promising technique for pattern ... See full document
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Support Vector Machines for Mandarin Phonetic Morse Code Recognition
... rehabilitation for people with various neuromuscular diseases, such as amyotrophic lateral sclerosis, multiple sclerosis, and muscular ...rate for persons with severe ...methods, support ... See full document
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Incremental Reduced Support Vector Machines
... Reduced Support Vector Machine (IRSVM) that starts with an extremely small reduced set and then sequentially expands to include informative data points into the reduced ...formative data ... See full document
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A Parallelized Learning Algorithm for Monotonicity Constrained Support Vector Machines
... improve support vector machines (SVMs) based on different scenarios of real world ...noisy data to obtain more useful results. For example, SVMs with monotonicity constraints and with ... See full document
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A Parallelized Learning Algorithm for Monotonicity Constrained Support Vector Machines
... • The training time of the parallel strategy RMC-SVM is less than RMC-SVM when both have the similarly classified results. The parallelized RMC-SVM with different part to divide datase[r] ... See full document
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Linear Convergence of a Decomposition Method for Support Vector Machines
... Recently the asymptotic convergence of some commonly used decomposition methods for support vector machines has been established. However, their local convergence rates are still unknown. In ... See full document
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A Guide to Support Vector Machines
... 3.3. MODEL SELECTION 33 the cross-validation rate. However, there are two motivations why we prefer the simple grid-search approach. One is that psychologically we may not feel safe to use methods which avoid doing an ... See full document
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Radius margin bounds for support vector machines with the RBF kernel
... In this paper, through the analyses why this bound performs well for LZSVM, we show that finding a bound whose minima are in a region with small loo values may be more impor[r] ... See full document
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Radius Margin Bounds for Support Vector Machines with the RBF Kernel
... implementations for solving these convex problems have been ...strategies. For small problems where cross validation is af- fordable, a complete search may be more ...But for large problems, using ... See full document
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