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Questions tagged [support-vector-machines]

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1answer
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How to derive the optimal bayesian solution to a model of two normal distributed populations

In the "Introduction" section of the paper Support-Vector Networks, it mentioned Fisher's solution to a model of two normal distributed populations: My questions are: How to derive equation (1)? I ...
5
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1answer
127 views

How to train a model to classify object trajectories?

I have a tracker that outputs the trajectory $(x,y,z)$ of an object (e.g., a can). I want to use these trajectories to train a classifier (i.e., SVM) in order to infer the activity that the person ...
0
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1answer
35 views

Support Vector Machines to use after Classification?

I'm a little bit about the SVM. I'm actually understanding for which purpose Support Vector Machines are used. The aim is to find the biggest (best) hyperplane between two different data clusters. The ...
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0answers
63 views

approximation of nonlinear time-dependent system with history

I have two time-dependent coupled equations. One of which is several orders of magnitude more computationally demanding than the other. I am trying to use machine learning to reproduce the behavior of ...
0
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1answer
67 views

SVM Math Question

I'm studying support vector machines and came across this paper. The following equation doesn't make sense to me, especially the part with the 0 ∀i. Any help understanding the basics of SVMs? yi * (...
6
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1answer
156 views

Support Vector Machines as Neural Nets?

This is more of a conceptual question. I have learned about Neural Nets, and I have some clue as to how Support Vector Machines work. I read somewhere however that given the appropriate kernel (is ...
3
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1answer
1k views

How to train an L2-regularized L1 Hinge Loss SVM using vowpal wabbit?

I'm trying to train an L2-regularized L1-hinge loss SVM using vowpal-wabbit. I use the following commands to train and test on the splice dataset: ...
9
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2answers
2k views

Do RBF kernel matrices tend to be ill-conditioned?

I use RBF kernel function to implement one kernel based machine learning algorithm(KLPP), the resulting kernel matrix $K$ $$K(i,j)= \exp\left({\frac{-(x_{i}-x_{j})^2}{ \sigma_{m}^2}}\right)$$ is ...
2
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1answer
41 views

Why do some known kernel functions manage to achieve linear separation in feature space?

I'm currently learning the maximal margin classifier with kernels, and I'm wondering - why do they work? In which cases do they work best? I'm especially interested in the RBF and polynomial kernels, ...
1
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1answer
69 views

Confusion related to svm optimization

I was reading thispaper related to kernel SVM. It states Support Vector Machine (SVM) (Cortes and Vap- nik, 1995) as the state-of-the-art classification algo- rithm has been widely applied in ...
7
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2answers
148 views

Why is the Dual problem preferred for Maximal Margin Classification?

The primal problem is $$\min_{w,b}\frac{1}{2}w^Tw$$ $$s.t. f_i(w)=1-y_i(w\cdot x_i+b)\leq0$$ Where $y_i=\pm1$. Instead of using Gradient Descent directly, the dual is usually solved: $$\max_{\...
0
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1answer
54 views

Confusion related to convexity of a problem

I was reading this paper related to Multiclass Classification with Multi-Prototype Support Vector Machines - paper However, I am having difficulty in understanding why they have mentioned the ...
6
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1answer
175 views

Confusion related to convexity and concavity of a function

I was reading this paper http://www.ist.temple.edu/~vucetic/documents/wang11kdd.pdf related to adaptive multi-hyperplane machine for non linear classification In that paper, they have mentioned about ...
10
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2answers
2k views

Calculating Lagrange coefficients for SVM in Python

I'm trying to write a full SVM implementation in Python and I have a few issues computing the Lagrange coefficients. First let me rephrase what I understand from the algorithm to make sure I'm on the ...