Linked Questions

I do understand that SVM is about finding the classifier that maximize the margin. But what is the intuition there? Please don't go into the math. Thx More specifically, if someone ask you during an ...

Good morning, For a homework I used a support vector machines (classification) with a RBF kernel, k-fold cross validation=10, cost of constraints violation=100, and gamma=0.001. It works great and ...

I try to get the basic understanding behing SVM algorithm, however I have a problem with basic mathematics. I follow the lecture Support Vector Machine. Suppose the two classes can be separated by ...

I'm trying to understand the SVM algorithm but not able to understand what weight vector and bias is ? Could anyone explain it in laymen terms.

I have no math or computer science training. When I run predict(svm,data,type="class") R spits out a prediction of 1 or 0 for each row of data. What is it doing ...

I am currently using an SVM with a linear kernel to classify my data. There is no error on the training set. I tried several values for the parameter $C$ ($10^{-5}, \dots, 10^2$). This did not ...

When using SVM, we need to select a kernel. I wonder how to select a kernel. Any criteria on kernel selection?

I am trying to interpret the variable weights given by fitting a linear SVM. (I'm using scikit-learn): ...

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. ...

Can someone please give me some intuition as to when to choose either SVM or LR? I want to understand the intuition behind what is the difference between the optimization criteria of learning the ...

Given the data points $x_1, \ldots, x_n \in \mathbb{R}^d$ and labels $y_1, \ldots, y_n \in \left \{-1, 1 \right\}$, the hard margin SVM primal problem is $$ \text{minimize}_{w, w_0} \quad \frac{1}{2} ...

What is the intuition behind the fact that an SVM with a Gaussian Kernel has infinite dimensional feature space?

I am using SVM for classification and I am trying to determine the optimal parameters for linear and RBF kernels. For the linear kernel I use cross-validated parameter selection to determine C and for ...

In the context of machine learning and pattern recognition, there's a concept called Kernel Trick. Facing problems where I am asked to determine whether a function could be a kernel function or not, ...

The kernel trick is used in several machine learning models (e.g. SVM). It was first introduced in the "Theoretical foundations of the potential function method in pattern recognition learning" paper ...