Tagged Questions

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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What do “real values” refer to in supervised classification?

I'm using supervised classification algorithms from mlpy to classify things into two groups for a question-answering system. I don't really know how these algorithms work, but they seem to be doing ...
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Information on how value of k in k-fold cross-validation affects resulting accuracies

I've been doing some Machine Learning, and have been using k-fold cross-validation to assess the generalisation performance of the algorithm. I've tried k-fold cross-validation with k = 5 and k = 200 ...
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Number of eigenfunctions for kernel

While studying machine learning, I've read the following statement: The kernel $K(x,y)=(x\cdot y+1)^d$ , for $x, y \in \mathbb{R}^p$, has $M={p+d \choose d}$ eigenfunctions that span the space of ...
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Active learning using SVM Regression

I have trained an SVM Regression model using training data, $x_1,x_2,\dots,x_N$. I want to perform active learning to improve the model; i.e., I want to add more samples to the training data and ...
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How does one appropriately apply cross-validation in the context of selecting learning parameters for support vector machines?

The wonderful libsvm package provides a python interface and a file "easy.py" that automatically searches for learning parameters (cost & gamma) that maximize the accuracy of the classifier. ...
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Can anyone explain why I have obtained an anti-predictive Support Vector Machine?

I'm playing with support vector machines (SVM) using the e1071::svm() function in R, and I encountered a scenario where I asked it for a leave-one-out cross-validated classification of a 2-category ...
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$\nu$-svm parameter selection

For the $\nu$-SVM (for both classification and regression cases) the $\nu \in (0;1)$ should be selected. The LIBSVM guide suggests to use grid search for identifying the optimal value of the $C$ ...
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Implementing the 'kernel trick' for a support vector machine in R

I've heard a bit about the 'kernel trick' for support vector machines, and I was wondering: How do you identify problems that might benefit from the kernel trick? How to implement it in R? Thank ...
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Linear behaviour of nonlinear SVM in higher dimensional space

I am taking a course in data mining. I am not sure how a non linear SVM when transformed to high dimensional space becomes a linear classification problem. It would be good if someone can provide me ...
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Dual problem for L2 support vector machine

Here is the dual problem for L2 support vector machine: $$\max_{\alpha\in\mathbb{R}^{n}} 2\alpha^{T}y-\alpha^{T}\left(K+n\lambda Id_{\mathbb{R}^{n}}\right)\alpha$$ \forall i\in\left\{ ...
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Efficient way to classify with SVM

I'm doing a binary classification using SVM classfier, libsvm, where roughly 95% belongs to one class. The parameters C and gamma are to be set before the actual training takes place. I followed the ...
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Should an SVM grid search show a high-accuracy region with low accuracies around?

I have 12 positive training sets (cancer cells treated with drugs with each of 12 different mechanisms of action). For each of these positive training sets, I would like to train a support-vector ...
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Understanding SVM regression: objective function and “flatness”

SVMs for classification make intuitive sense to me: I understand how minimizing $||\theta||^2$ yields the maximum margin. However, I don't understand that objective in the context of regression. ...
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What is the connection between Kernel Logistic Regression and Smoothing Splines?

Working on probabilistic outputs of kernel methods I found the formulation of the SVM as a Penalized Method using the Binomial Deviance (described for example in "The Elements of Statistical Learning ...
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Which kernel method gives the best probability outputs?

Recently I have used Platt's scaling of SVM-outputs to estimate probabilities of default-events. More direct alternatives seem to be "Kernel logistic Regression" (KLR) and the related "Import Vector ...
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Computing the decision boundary of a linear SVM model

Given the support vectors of a linear SVM, how can I compute the equation of the decision boundary?
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Output of linear SVM model in Matlab using SVM-light

I am using SVM-light with Matlab, for linear SVM. I would like to understand the output model, but I cannot find any documentation or help about it. Here is the output: ...
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VC dimension of SVM with polynomial kernel in $\mathbb{R^{2}}$

What is the VC dimension of SVM with the polynomial kernel $k(x,x')=(1+<x,x'>_{\mathbb{R^{2}}})^{2}$ for binary classification in $\mathbb{R^{2}}$? It would be equal or more than v iff ...
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Help me understand Support Vector Machines

I understand the basics of what a Support Vector Machines' aim is in terms of classifying an input set into several different classes, but what I don't understand is some of the nitty-gritty details. ...
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Variable importance from SVM

How to obtain a variable (attribute) importance using SVM?