Learning And Optimization Of The Kernel Functions From Insufficiently Labeled Data

Amongst all the machine learning techniques, kernel methods are increasingly becoming popular due to their efficiency, accuracy and ability to handle high-dimensional data. The fundamental problem related to these learning techniques is the selection of the kernel function. Therefore, learning the kernel as a procedure in which the kernel function is selected for a particular dataset is highly important. In this thesis, two approaches to learn the kernel function are proposed: transferred learning of the kernel and an unsupervised approach to learn the kernel. The first approach uses transferred knowledge from unlabeled data to cope with situations where training examples are scarce. Unlabeled data is used in conjunction with labeled data to construct an optimized kernel using Fisher discriminant analysis and maximum mean discrepancy. The accuracy of classification which indicates the number of correctly predicted test examples from the base kernels and the optimized kernel are compared in two datasets involving satellite images and synthetic data where proposed approach produces better results. The second approach is an unsupervised method to learn a linear combination of kernel functions