Machine Learning

Integration of supervised and unsupervised learning

In machine learning for classification problems, there are two distinct approaches to learning or classifying data: the supervised learning and un-supervised learning. The supervised learning deals with problem where a set of data are labeled for training and another set of data would be used for testing. The un-supervised learning deals with problem where none of the labels of the data are available. In recent years, important classification tasks have emerged with enormous volume of data. The labeling of a significant portions of the data for training is either infeasible or impossible. Sufficient labeled data for training are often unavailable in data mining, text categorization and web page classification.

Kernel Methods and Graph Networks for Machine Learning

Kernel methods and graph networks are powerful approaches for solving various machine learning problems. In particular, randomized methods for optimizing criterion function on graph networks are interesting solutions to various NP-hard learning problems.

Demonstration for the Randomized Contraction methods for Machine Learning.