Adaptively Weighted Large Margin Classifiers
Yichao Wu,Yufeng Liu +1 more
TL;DR: A new weighted large margin classification technique is proposed that is robust to outliers and thus is able to produce more accurate classification results.
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Abstract: Large margin classifiers have been shown to be very useful in many applications The support vector machine is a canonical example of large margin classifiers Despite their flexibility and ability in handling high-dimensional data, many large margin classifiers have serious drawbacks when the data are noisy, especially when there are outliers in the data In this article, we propose a new weighted large margin classification technique The weights are chosen adaptively with data The proposed classifiers are shown to be robust to outliers and thus are able to produce more accurate classification results
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Citations
Robust support vector machines based on the rescaled hinge loss function
TL;DR: A new robust SVM is developed based on the rescaled hinge loss function which is a monotonic, bounded and nonconvex loss that is robust to outliers and can help explain the robustness of iterative weighted SVM from a loss function perspective.
111
Probability estimation with machine learning methods for dichotomous and multicategory outcome: Theory
Jochen Kruppa,Yufeng Liu,Gérard Biau,Gérard Biau,Michael Kohler,Inke R. König,James D. Malley,Andreas Ziegler +7 more
TL;DR: The general validity of the machine learning methods is demonstrated and each method fails in at least one simulation scenario, and recommendations for selecting and tuning the methods are given.
101
Robust support vector machines for classification with nonconvex and smooth losses
TL;DR: This study proposes an iteratively reweighted type algorithm and provides a constructive proof of its convergence to a stationary point and shows that in each iteration, it is a quadratic programming problem in its dual space and can be solved by using state-of-the-art methods.
Automatic segmentation of white matter hyperintensities from brain magnetic resonance images in the era of deep learning and big data - A systematic review.
TL;DR: It is found that data and code availability, bias in study design and ground truth generation influence the wider validation and applicability of these methods in clinical research, and deep learning methods are not favoured.
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Robust statistics-based support vector machine and its variants: a survey
Manisha Singla,Kaushal K. Shukla +1 more
TL;DR: An up to date survey of the research development in the field of robustness in SVM and its extensions and includes a discussion part which discusses the pros and cons of the proposed approaches and highlights some important future directions in it.
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