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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://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.ncbi.nlm.nih.gov/pmc/articles/PMC3668975/
Apr 01, 2013 · Functional robust support vector machines for sparse and irregular longitudinal data ... we propose functional robust truncated-hinge-loss support vector machines to perform multicategory classification with the aid of functional principal component analysis. ... we call this type of data sparse and irregular functional data or, interchangeably ...Cited by: 19
https://www4.stat.ncsu.edu/~lu/ST7901/reading%20materials/Robust%20Truncated%20Hinge%20Loss%20Support%20Vector%20Machines.pdf
Robust Truncated Hinge Loss Support Vector Machines Yichao W U and Yufeng L IU The support vector machine (SVM) has been widely applied for classiÞcation problems in both machine …
https://www.sciencedirect.com/science/article/pii/S0925231219310793
A novel twin support vector machine method is presented. • A robust optimization scheme is used to derive second-order cone programming models. • The proposal extends the nonparallel support vector machine approach. • Best performance is achieved in experiments on benchmark datasets.Author: Julio López, Sebastián Maldonado, Miguel Carrasco
https://arxiv.org/abs/1409.0934
Abstract: The support vector machine (SVM) is one of the most successful learning methods for solving classification problems. Despite its popularity, SVM has a serious drawback, that is sensitivity to outliers in training samples. The penalty on misclassification is defined by a convex loss called the hinge loss, and the unboundedness of the convex loss causes the sensitivity to outliers.Author: Takafumi Kanamori, Shuhei Fujiwara, Akiko Takeda
https://www.tandfonline.com/doi/abs/10.1080/10618600.2012.680823
(2013). Functional Robust Support Vector Machines for Sparse and Irregular Longitudinal Data. Journal of Computational and Graphical Statistics: Vol. 22, No. 2, pp. 379-395.Cited by: 19
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
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
http://jmlr.csail.mit.edu/papers/volume10/xu09b/xu09b.pdf
Keywords: robustness, regularization, generalization, kernel, support vector machine 1. Introduction Support Vector Machines (SVMs for short) originated in Boser et al. (1992) and can be traced back to as early as Vapnik and Lerner (1963) and Vapnik and Chervonenkis (1974). They continue to be one of the most successful algorithms for ...
http://proceedings.mlr.press/v38/katsumata15.pdf
Robust Cost Sensitive Support Vector Machine er. Owing to this, although connections between the robust and regularized classi ers has been known to some extent [11, 2], not many works concentrating on the explicit relationship between them have been made. We also point out that, to the best of our knowl-edge, in previous robust SVM models ...
https://www.researchgate.net/publication/309183279_Robust_support_vector_machines_based_on_the_rescaled_hinge_loss_function
The support vector machine (SVM) is a popular classifier in machine learning, but it is not robust to outliers. In this paper, based on the Correntropy induced loss function, we propose the ...
https://pdfs.semanticscholar.org/204d/96c5fd36485a6cbdc96aeb20f2c52b9fc701.pdf
Support Vector Machine Classification of Uncertain and Imbalanced data using Robust Optimization RAGHAV PANT, THEODORE B. TRAFALIS, KASH BARKER School of Industrial Engineering University of Oklahoma . 202 W. Boyd Street, Room 124, Norman, Oklahoma - 73019 . UNITED STATES [email protected], [email protected], [email protected] . Abstract: - In this paper
https://dspace.cc.tut.fi/dpub/bitstream/handle/123456789/21387/Kaiser.pdf;sequence=3
support vector machine against outliers in the training data. In this thesis we will discuss the class robust support vector machines which aim to make the standard support vector machine robust against noise by implicit outlier ltering. Those ap-proaches are using the support vector machine to detect and remove outliers based
https://www.researchgate.net/publication/4030187_Robust_Optimization_in_Support_Vector_Machine_Training_with_Bounded_Errors
Trafalis and Alwazzi [14] proposed a robust support vector machine (SVM) classifier that studies noisy data with bounded errors on the linear model of SVM. Their work investigated how the ...
http://www.support-vector-machines.org/SVM_regression.html
SVM, support vector machines, SVMC, support vector machines classification, SVMR, support vector machines regression, kernel, machine learning, pattern recognition ...
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 …
http://www.intjit.org/cms/journal/volume/11/9/119_5.pdf
In this paper combining robust estimation with wavelet support vector machine (WSVM), a robust wavelet support vector regression (WSVR) model is developed. Firstly, a new type of wavelet support vector machine is proved and used to determine appropriate WN structure and initial parameters.
https://www.svm-tutorial.com/2014/10/support-vector-regression-r/
If you use a support vector machine you will be performing support vector regression, not multiple linear regression. You can give a vector as input to perform multivariate support vector regression if you wish. Jun July 24, 2017 at 3:20 pm. Thank you for your post. I learned a lot from the tutorial.
http://www.public.asu.edu/%7Ehuanliu/papers/icml04.pdf
Robust Feature Induction for Support Vector Machines Rong Jin [email protected] Department of Computer Science and Engineering, Michigan State University, East Lansing, MI48824 Huan Liu [email protected] Department of Computer Science and Engineering, Arizona State University, Tempe, AZ85287-8809 Abstract The goal of feature induction is to
https://www.analyticsvidhya.com/blog/2017/09/understaing-support-vector-machine-example-code/
Sep 13, 2017 · In this article, we looked at the machine learning algorithm, Support Vector Machine in detail. I discussed its concept of working, process of implementation in python, the tricks to make the model efficient by tuning its parameters, Pros and Cons, and finally a problem to solve.
https://arxiv.org/pdf/1009.5818.pdf
insight in the data. In robust multivariate statistics outlier maps are quite popular tools to assess the quality of data under consideration. They provide a visual representation of the data depicting several types of outliers. This paper proposes an outlier map designed for Support Vector Machine classification. The Stahel–Donoho outlying-
https://pythonmachinelearning.pro/classification-with-support-vector-machines/
One of the most widely-used and robust classifiers is the support vector machine. Not only can it efficiently classify linear decision boundaries, but it can also classify non-linear boundaries and solve linearly inseparable problems. We’ll be discussing the inner …
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