Just to clarify, these contents are mainly summarized from the course I took: “Fundamental of Big Data Analytics”, taught by Prof. Mathar Rudolf. For for information please visit: https://www.ti.rwthaachen.de
In the last post, we discussed that the SVM optimization problem is:
To solve this optimization problem, there are multiple ways. One way is to treat this problem as a standard optimization problem and use gradient descent algorithm to compute the optimal parameters. Another way is to formulate the Lagrangian dual problem of the primal problem, transferring original optimization problem into an easier problem. Here we mainly discuss the first method.
Gradient Descent Algorithm
To apply GD, we need to design a new objective function which is differentiable. The new objective function is:
This objective function contains two terms. The first term is used to maximize the margin. This term is also called regularization term. The second term is a penalty term used to penalize the case where , which represents incorrect/imperfect classification. Note that for the case we don’t need to penalize it, so we use a max function . This is also called hinge loss.
is a weight parameter used to control the weight of the regularization term. If is too small, the model (the learned hyperplane) will mainly focuses on correctly classify the training data, but the margin may not be maximized. If is too large, the model will have have a large margin, while there may exist more missclassified points in the training dataset.
Compute the gradient
To apply GD we also need get the exact expression of the gradient.
where
The updating rules of the parameter and are:
where is the learning rate.
Note that in practice that in each update loop we may not use the whole training dataset, instead we may use a minibatch. Suppose that the mini batch size is , then the expression of the gradient is:
In the following we will use this minibatch style expression.
Code Implementation
To test the GD algorithm, we use toy data shown in figure 2d toy data
Experiment and Analysis
Visualization of Hyperplane
In this part, we set . The change of the hyperplane over iterations is shown in figure Hyperplane Over Iteration
We can also test the influence of on the final results of the hyperplane, to check if our illustration on above is right or not. The results are shown in figure Influence Of Lambda.
We also noticed that when is extremely small, like 1e5, the margin doesn’t become further smaller. Actually we tested that even if we will still get the same ideal results, which implies that the regularization term in the loss function is useless in this toy example! This may be due to the fact that for such a simple dataset, it is very easy to find the optimal separating hyperplane and support vectors. Once the optimal separating hyperplane is found, the model will stick to it even if there is no regularization term in the loss function, since in this case the gradient is 0, and the training is actually stopped.
Use SVM for classification
Suppose that we have obtained the optimal and , given a new input data , we can make a decision of the label in two ways:
Hard Decision
Soft Decision
where
So that’s it. Now we are able to use GD to train a SVM model and used it for classification task. In the next post we will explore more possibilities of the solutions on SVM.

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