Answer Posted / Shashi Shekhar Sisodiya

The kernel trick is a technique used in Support Vector Machines (SVM) to handle non-linearly separable data. It maps the input data from the original space into a higher dimensional feature space where the data becomes linearly separable. This transformation is done using a positive definite kernel function like polynomial, Gaussian radial basis function, or sigmoid. The kernel trick allows us to solve complex non-linear problems using SVM without having to explicitly find the mapped feature space.

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

208

Tell us do you have research experience in machine learning?

149

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

74

Do you have research experience in machine learning?

73