What are the disadvantages of an SVM hard margin classifier?
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30 Aug 2009 | We propose an extension of the standard SVM optimization in which we also account for the radius in order to produce an even tighter error bound than what we get by controlling only for the margin. |
14 Jan 2021 33 Citations | As a further outcome, the analysis allows for the identification of the maximum number of training samples that the hard-margin SVM is able to separate. |
Our formulation generalizes the traditional large margin principle used in standard SVM, that is, we maximize the margin-radius-ratio. | |
177 Citations | Results indicate that the performance of SVM classifier is better than other machine learning-based classifiers. |
11 Jun 2017 41 Citations | The experimental results show that the offered approach allows increasing the classification quality of the SVM classifier. |
We propose a novel online kernel classifier algorithm that converges to the Hard Margin SVM solution. | |
03 Sep 2008 | Recent results in theoretical machine learning seem to suggest that nice properties of the margin distribution over a training set turns out in a good performance of a classifier. |
14 Jan 2021 33 Citations | The precise nature of our results allows for an accurate performance comparison of the hard-margin and soft-margin SVM as well as a better understanding of the involved parameters (such as the number of measurements and the margin parameter) on the classification performance. |
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