Questions tagged [max-margin]

The tag has no usage guidance.

Filter by
Sorted by
Tagged with
22 votes
1 answer

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 ...
Amit's user avatar
  • 803
11 votes
1 answer

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 ...
Simon's user avatar
  • 288
6 votes
2 answers

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: This requires me to be able to remove individual trees from the forest and ...
Spy_Lord's user avatar
  • 347
3 votes
1 answer

Relationship between L1 penalty and margin in SVM

Expanding on "Why aren't there there two regularization terms in SVC?" and "Meaning of penalty and loss in ...
Igor F.'s user avatar
  • 8,605
3 votes
1 answer

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 ...
etarion's user avatar
  • 131
2 votes
0 answers

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, ...
emrea's user avatar
  • 1,031
1 vote
1 answer

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 ...
maso's user avatar
  • 1,309
1 vote
1 answer

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 ...
Veech's user avatar
  • 210
1 vote
0 answers

Show that solution for the maximum margin hyperplane is unchanged when w.x + b = (+/-) 1 is replaced by arbitrary constant $\gamma$?

How to show that solution for the maximum margin hyperplane for hard-margin SVM is unchanged when w.x + b = (+/-) 1 is replaced by arbitrary constant $\gamma$? In the derivation for the SVM, we ...
Tuhin Dutta's user avatar
0 votes
1 answer

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: I have the need to calculate the margin. Namely, given a training set of ...
Nick's user avatar
  • 133
0 votes
0 answers

Interpretation/Intuition for L2 Regularization in Neural Networks [duplicate]

When we use L1-regularization in neural networks, it is pretty intuitive how the regularization will influence the learned weights. Namely, weights will not become needlessly large and unimportant ...
user2611844's user avatar
0 votes
0 answers

Does gradient descent for linear regression selects the minimal norm solution?

I was told that Gradient Descent finds the weights of smallest norm. This is what I understood in the linear regression setting: $f_w(x)=w^\top x$ are the linear functions $ \mathbb{R}^n \rightarrow \...
rod's user avatar
  • 101