Searching for Modified Support Vector Regression In Outlier Detection information? Find all needed info by using official links provided below.
https://www.researchgate.net/publication/224181477_Modified_Support_Vector_Regression_in_outlier_detection
Although the standard Support Vector Regression based outlier detection methods for non-linear function with multidimensional input have achieved good performance, they have practical issues in ...
https://www.researchgate.net/publication/269977161_Outlier_Detection_Using_Support_Vector_Machines
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https://www.researchgate.net/publication/220643885_Modified_support_vector_novelty_detector_using_training_data_with_outliers
This paper proposes the modified support vector novelty detector (SVND) for novelty detection which addresses the problem of detecting outliers from normal data patterns.
https://stats.stackexchange.com/questions/104348/outlier-detection-using-regression
Your best option to use regression to find outliers is to use robust regression. Ordinary regression can be impacted by outliers in two ways: First, an extreme outlier in the y-direction at x-values near $\bar x$ can affect the fit in that area in the same way an outlier can affect a mean.
https://www.researchgate.net/publication/4116917_Robust_outlier_detection_using_SVM_regression
Nevertheless, the techniques of the standard support vector regression (-SVR) and modified support vector regression achieved good performance for outliers' detection for nonlinear functions with ...
http://yadda.icm.edu.pl/yadda/element/bwmeta1.element.ieee-000005596976
Although the standard Support Vector Regression based outlier detection methods for non-linear function with multidimensional input have achieved good performance, they have practical issues in computational costs and parameter adjustment.
https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3041295/
Nov 13, 2010 · To evaluate the performance of classification via outlier detection by one-class support vector machines (SVMs) as a means of identifying abnormal cases in the domain of melanoma prognosis. Methods: Empirical evaluation of one-class SVMs on a data set for predicting the presence or absence of metastases in melanoma patients, and comparison with ...Cited by: 15
https://en.wikipedia.org/wiki/Support-vector_machine
The soft-margin support vector machine described above is an example of an empirical risk minimization (ERM) algorithm for the hinge loss. Seen this way, support vector machines belong to a natural class of algorithms for statistical inference, and many of its unique features are due to …
https://www.researchgate.net/publication/326114971_Fixed_Parameters_Support_Vector_Regression_for_Outlier_Detection
Request PDF Fixed Parameters Support Vector Regression for Outlier Detection The support vector machine (SVM) is currently a very popular technique of outlier detection as it is a robust model ...
https://www.sciencedirect.com/science/article/pii/S1474034613000232
Support vector regression for anomaly detection from measurement histories. ... The emphasis in this paper will be on the concept of employing support vector regression (SVR) for predicting the thermal response of bridges and on the post-processing of SVR output. The following section describes the approach for developing SVR models that form ...Cited by: 19
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