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Results tagged with svm
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user 30621
Support Vector Machine refers to "a set of related supervised learning methods that analyze data and recognize patterns, used for classification and regression analysis."
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Vector Valued SVM
Actually you can train an SVM which can output vectors and more. As was noted by Marc Claesen such outputs are called structured output, and are formulated in terms of structured output SVM. …
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2
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Different formulations for SVM with slack variables (primal)
In original paper on soft-margin SVM there were no $\frac{1}{n}$, although it still won't matter because that coefficient is non-negative. …
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9
votes
Accepted
Deriving the optimal value for the intercept term in SVM
Think of SVM as a maximum margin classifier. In that sense we seek separating hyperplane which will be equidistant from all negative and all positive examples. …
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3
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Problems with Implementing SVM in CVX - lagrange variables alpha are not what I expected
Define your kernel only in terms of $X_i$, that is $$Q(x_i,x_j;c,d)=(x_i^T x_j+c)^d $$
where $c,d$ are kernel parameters. Then, replace this line
minimize (0.5.*quad_form(alpha,Q) - ones(m,1)'*alpha …
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1
vote
Parameter selection in multiple kernel learning
One way to do it is to represent a kernel as a convex combination of kernels having different parameters and have MKL algorithm decide which kernels to use. Here is great paper on MKL algorithms:
Son …
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2
votes
What is parameter fine tuning means in SVM?
If you train soft-margin SVM for such data set resulting hyperplane will equally penalize both negative or positive examples. … In the end a way to choose parameters for such non-generic SVM is called parameter fine-tuning. …
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2
votes
Accepted
How to give the input of an image to SVM after preprocessing
Once such conversion is possible you can create $k$ dimensional representation for both training and testing images and learn SVM. …
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2
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Returning the inverse of a matrix in a quadratic program (SVM) in cvx optimization package
Optimization software needs to find inverse of the Hessian of the objective function which, in this case, coincides with inverting $Q$. This is by far the most costly operation. Here few things which …
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44
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One-vs-All and One-vs-One in svm?
This often leads to imbalanced datasets meaning generic SVM might not work, but still there are some workarounds. …
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0
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Why not just dump the neural networks and deep learning?
Linear and SVM do not have this mathematical drawbacks and are fully consistent with a a set of mathematical equations. … Why not just think on same lines (need not be linear though) and come up with a new ML model better than Linear and SVM and neural networks and deep learning? …
13
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The difference of kernels in SVM?
From practical point of view consult the following page:
How to select kernel for SVM? …
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