Create README.md

Author: mmarouen

README.md

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+# SVM_Framework
+Support vector machines flexible framework  
+We solve the unconstrained primal SVM formulation    
+SVM & Softmax classifiers supported  
+NB:  
+-Softmax classifier refers to penalized and kernalized logistic regression  
+-Classical logistic regression can be obtained by setting cost very high & using a linear kernel  
+-Python implementation in a seperate repository  
+# 1. Classifiers/regressors:  
+LS: regression classifier using penalized least squared loss  
+Softmax: Softmax classifier using cross entropy loss  
+SVM:svm classifier using quadratic hinge loss  
+# 2. Optimization methods:  
+BGD: gradient descent (batch)  
+NGD: Newton-Raphson optimization (batch)  
+CGD: conjugate gradient descent (batch)    
+SGD: stochastic gradient descent (under development)  
+# 3. Kernels:  
+gaussian: gaussian kernel  
+linear: linear kernel  
+poly:polynomial kernel  
+For any remarks please let me know <azzouz.marouen@gmail.com>