Questions tagged [max-margin]

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IS optimization unnecessary in SVM?

According to here, Now knowing the $a_i$ we can find the weights $w$ for the maximal margin separating hyperplane: \begin{align*} w = \sum_{i=1}^{l} a_i y_i x_i \end{align*} I cannot understand what ...
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1answer
2k views

How to calculate the margin in SVM light?

I'm using Support Vector Machine in a project. The library chosen is SVM light of Joachims: http://svmlight.joachims.org/ I have the need to calculate the margin. Namely, given a training set of ...
9
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1answer
3k views

What's the relationship between an SVM and hinge loss?

My colleague and I are trying to wrap our heads around the difference between logistic regression and an SVM. Clearly they are optimizing different objective functions. Is an SVM as simple as saying ...
1
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1answer
662 views

CRF Training: Max-margin vs max-likelihood

I'm trying to use PyStruct's CRF implementation. In its user guide, it says the following: I call these models Conditional Random Fields (CRFs), but this a slight abuse of notation, as PyStruct ...
5
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2answers
865 views

Is there a way to remove individual trees from a forest in the randomForest package in R?

I am trying to implement the ideas in this paper: http://www.sciencedirect.com/science/article/pii/S0925231212003396. This requires me to be able to remove individual trees from the forest and ...
2
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0answers
214 views

Linear SVM decision boundary after a linear transformation of data

Let $w$ be the decision boundary of a linear SVM trained on the dataset $D=\{(x_i, y_i)_{i=1}^N\}$. Suppose we apply a linear transformation A to examples $x_i$s and obtain a new dataset $D'=\{(z_i, ...
15
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1answer
12k views

Interpreting distance from hyperplane in SVM

I have a few doubts in understanding SVMs intuitively. Assume we have trained a SVM model for classification using some standard tool like SVMLight or LibSVM. When we use this model for prediction ...
3
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1answer
150 views

Max-margin clustering with size constraint

Given a dataset $D$ and a distance measure, I want to split the dataset into two disjoint subsets $X, Y$ of a specified size (say 80% and 20% of the original size), so that the minimum distance of all ...