Answer Posted / Anurag Kumar

Kernel SVM is a variant of support vector machines that can handle non-linearly separable data using a technique called the kernel trick. The kernel function maps the input data into a higher-dimensional space where it becomes linearly separable, and then finds the optimal hyperplane in this space. Commonly used kernels include linear, polynomial, radial basis function (RBF), and sigmoid.

Do you have research experience in machine learning?

73

Tell me what are the last machine learning papers you've read?

209

What are standardization and normalisation? Give one advantage of each over the other?

74

Tell us do you have research experience in machine learning?

150