In this tutorial, we'll describe the mathematics behind Support Vector Machines. This tutorial is going to be much more math heavy as compared to other tutorials. If you're mostly interested in applying SVMs to solve problems, then our first tutorial on SVMs is sufficient. However, if you would like to understand the mathematical basis of Support Vector Machines, then you'll find this tutorial interesting.
In this tutorial, we will focus on the hard-margin SVM and soft-margin SVM. However, we will not be considering kernels or the hyper-parameter \gamma (gamma).
In SVM, the decision function for predicting data points x corresponds to the equation of an hyperplane:
Indian Digits Dataset via CMATERdb in easy to use NumPy format
CMATERdb is the pattern recognition database repository created at the 'Center for Microprocessor Applications for Training Education and Research' (CMATER) research laboratory, Jadavpur University, Kolkata 700032, INDIA. This database is free for all non-commercial uses. More information on CMATER is here.
CMATERdb 3.1.1: Handwritten Bangla numeral database is a balanced dataset of total 6000 Bangla numerals (32x32 RGB coloured, 6000 images), each having 600 images per class (per digit).
CMATERdb 3.2.1: Handwritten Devanagari numeral database is a balanced dataset of total 3000 Devanagari numerals (32x32 RGB coloured, 3000 images), each having 300 images per class (per digit).
CMATERdb 3.4.1: Handwritten Telugu numeral database is a balanced dataset of total 3000 Telugu numerals (32x32 RGB coloured, 3000 images), each having 300 images ...