Contents
Why SVM is effective in cases where the number of dimensions is greater than the number of samples?
Pros and Cons associated with SVM It is effective in cases where the number of dimensions is greater than the number of samples. It uses a subset of training points in the decision function (called support vectors), so it is also memory efficient.
What does hyperplane in SVM mean?
Now that we understand the SVM logic lets formally define the hyperplane . A hyperplane in an n-dimensional Euclidean space is a flat, n-1 dimensional subset of that space that divides the space into two disconnected parts. The line has 1 dimension, while the point has 0 dimensions.
How is the VC dimension related to SVS?
The VC dimension doesn’t map to the amount of SVs of a given solution. The VC dimension is the maximum number of dataset samples that can be shattered perfectly by the model for any combination of the labels associate with those points. On the other hand, support vectors are the points that define the hyperplane.
Is the VC dimension of the SVM model proof?
In fact, as far as I know, there is no known proof of the VC dimension of the SVM model (we know, that it is a gap tolerant classifier, which should have lower VC dimension, but it is far from being a dimension proof). The VC dimension doesn’t map to the amount of SVs of a given solution.
How are feature selection and kernels used in SVM?
The main objective in SVM is to find the optimal hyperplane to correctly classify between data points of different classes (Figure 2). The hyperplane dimensionality is equal to the number of input features minus one (eg. when working with three feature the hyperplane will be a two-dimensional plane).
What does SVM stand for in machine learning?
SVM: Feature Selection and Kernels. A Support Vector Machine (SVM) is a supervised machine learning algorithm that can be employed for both classification and regression purposes.