Hello, Everyone, I am beginner for Machine learning and at present, I am learning about the non-linear support vector classifier and I have my own dataset and I want to separate positive and negative points with best decision boundary using non-linear SVM . can anyone help me providing sufficient information to do it. Thanks in advance
Support vector machine has kernels that support non linear classification. The kenels are radial basis functions ( RBF), Polynomial and sigmoid functions. You can use any of these kernels for your non linear classification.