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: Real world data sets:
(a) Image data set for static pattern classification
(b) Image data set for varying length pattern (Set of local feature vectors representation) classification
Classifiers to be built for Dataset 1(a) :
1. K-nearest neighbours classifier, for K=1, K=7 and K=15
2. Naive-Bayes classifier with a Gaussian distribution for each class
a. Covariance matrix for all the classes is the same and is2I
b. Covariance matrix for all the classes is the same and is C
c. Covariance matrix for each class is different
Classifiers to be built for Dataset 1(b) :
1. K-nearest neighbours classifier, for K=1, K=7 and K=15
2. Bayes classifier with a GMM for each class, using full covariance matrices
3. Bayes classifier with a GMM for each class, using diagonal covariance matrices
4. Bayes classifier with K-nearest neighbours method for estimation of class-conditional probability density function, for K=10 and K=20
Classifiers to be built for datasets (a) and (b) in Dataset 2:
1. Bayes classifier with a GMM for each class, using full covariance matrices
2. Bayes classifier with a GMM for each class, using diagonal covariance matrices
Use the cross-validation method to choose the best values of hyperparameters.
