Description:
  -Implemented several Classifiers and K-Fold Cross Validation
  -Tested on THE MNIST DATABASE (http://yann.lecun.com/exdb/mnist/) and several datasets from https://archive.ics.uci.edu/ml/datasets.php
  
Input:  MNIST for the classifiers: K-Nearest Neighbors, Perceptron, SoftSVM
        iris.csv, wine.csv, adult.csv for Naive Bayes classifier
        
Output: Analysis on the accuracy of the classifiers using the test dataset

Classifiers Implemented:
  -K-Nearest Neighbors
  -Perceptron
  -SoftSVM using QP solver
  -SoftSVM using Stochastic Gradient Descent (sgd)
  -Naive Bayes (Gaussian)
  
Options:  The main file is "MachineLearning.java". All the input needed found at the first part of the file.
          You can change input file, activate Cross Validation, configuring different parameters for the classfiers 
          and different option for the MNIST dataset (size,binary).