Description
Dataset 1: 2-dimensional artificial data:
(a) Linearly separable data set for static pattern classification
(b) Nonlinearly separable data set for static pattern classification Dataset 2: Image data set for static pattern classification
Classifiers to be built for Dataset 1(a):
1. Perceptron for every pair of classes
2. Multilayer feedforward neural network (MLFFNN) with a single hidden layer for all classes
3. Linear SVM classifier for every pair of classes
Classifiers to be built for Dataset 1(b) :
1. MLFFNN with two hidden layers
2. Nonlinear SVM using one-against-the-rest approach : (a) Polynomial kernel, (b) Gaussian kernel
Classifiers to be built for Dataset 2:
1. MLFFNN with two hidden layers
2. Gaussian kernel based SVM using one-against-the-rest approach
Use the cross-validation method to choose the best values of hyperparameters.
