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http://www.cs.unc.edu/~jeffay/courses/nidsS05/ai/robust-anomaly-detection-using.pdf
Robust Anomaly Detection Using Support Vector Machines Wenjie Hu Yihua Liao V. Rao Vemuri Department of Applied Science Department of Computer Science Department of Applied Science University of California, Davis University of California, Davis University of California, Davis [email protected] [email protected] [email protected]
https://www.sciencedirect.com/science/article/pii/S0377221705006892
In this paper, we investigate the theoretical aspects of robust classification and robust regression using support vector machines. Given training data (x 1, y 1), … , (x l, y l), where l represents the number of samples, x i ∈ R n and y i ∈ {−1, 1} (for classification) or y i ∈ R (for regression), we investigate the training of a support vector machine in the case where bounded ...Cited by: 114
https://link.springer.com/chapter/10.1007/978-0-85729-504-0_5
Abstract. In real world applications, the training data are not usually assumed to be known exactly due to measurement and statistical errors. Since the solutions to optimization problems are typically sensitive to training data perturbations, errors in the input data tend to get amplified in the decision function, often resulting in far from optimal solutions.Cited by: 4
https://web.cs.ucdavis.edu/~vemuri/papers/rvsm.pdf
In this paper, we present a new approach, based on Robust Support Vector Machines (RSVMs) [9], to anomaly detection over noisy data. RSVMs effectively address the over-fitting problem introduced by the noise in the training data set. With RSVMs, the incorporation of an averaging technique in the standard support vector machines makes the ...
https://www.sciencedirect.com/science/article/pii/S1568494619302534
Further, two robust SVM frameworks are presented to handle robust classification and regression problems by applying L q-loss to support vector machine, respectively. Last but not least, we demonstrate that the proposed classification framework satisfies Bayes’ optimal decision rule.Cited by: 2
http://jmlr.csail.mit.edu/papers/volume10/xu09b/xu09b.pdf
We consider regularized support vector machines (SVMs) and show that they are precisely equiva-lent to a new robust optimization formulation. We show that this equivalence of robust optimization and regularization has implications for both algorithms, and analysis. In terms of algorithms, the
https://www.sciencedirect.com/science/article/pii/S0893608019300309
Twin support vector machine (TWSVM) is a classical and effective classifier for binary classification. However, its robustness cannot be guaranteed due to the utilization of squared L2-norm distance that can usually exaggerate the influence of outliers.Cited by: 3
https://link.springer.com/content/pdf/10.1007%2Fs10107-017-1209-5.pdf
Nov 29, 2017 · The support vector machine (SVM) is one of the most popular classification methods in the machine learning literature. Binary SVM methods have been extensively studied, and have achieved many successes in various disciplines. However, generalization to multicategory SVM (MSVM) methods can be very challenging. Many existing methods estimate k functions for k classes with an explicit sum …Cited by: 4
https://www.sciencedirect.com/science/article/pii/S0031320312002890
In this paper, we proposed a new robust twin support vector machine (called R-TWSVM) via second order cone programming formulations for classification, which can deal with data with measurement noise efficiently.Preliminary experiments confirm the robustness of the proposed method and its superiority to the traditional robust SVM in both computation time and classification accuracy.Cited by: 261
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