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For questions about support vector machines (SVMs), which are supervised learning models with associated learning algorithms that analyze data used for classification and regression analysis.

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A neural network without a hidden layer is the same as just linear regression. If I then use squared hinge loss and encoporate the l2 regularisation term, is it fair to then call this network the s …
asked Apr 27 '20 by FeedMeInformation
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The objective function of an SVM is the following: $$J(\mathbf{w}, b)=C \sum_{i=1}^{m} \max \left(0,1-y^{(i)}\left(\mathbf{w}^{t} \cdot \mathbf{x}^{(i)}+b\right)\right)+\frac{1}{2} \mathbf{w}^{t} \cdo …
asked Apr 11 '20 by FeedMeInformation
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I have created a CNN for use on the MNIST dataset for now (so I have 10 classes). I have trained SVMs on the sublayers of this trained CNN and wish to combine them into a combined SVM as to give a com …
asked Dec 29 '19 by FeedMeInformation
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If I have the Gaussian kernel $$ k(x, x') = \operatorname{exp}\left( -\| x - x' \|^2 / 2\sigma^2 \right) $$ What is $x$ and $x'$ in the context of training an SVM?
asked Mar 1 '20 by FeedMeInformation
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I have an SVM currently and want to perform a gradient based attack on it similar to FGSM discussed in Explaining And Harnessing Adversarial Examples. I am struggling to actually calculate the gr …
asked Mar 11 '20 by FeedMeInformation