SEMINAR NOTICE

Ensemble Learning and Evolutionary Algorithms

Dr. Rammohan Mallipeddi, Kyungpook National University, Korea.

DATE & TIME : 23 January 2015, 4.00 PM

VENUE: Semianr Room, SCIS

ABSTRACT

According to “No Free Lunch” theorem, without the prior knowledge of environment, no single method or scheme is superior to the other. For instance in machine learning it has been shown that the prediction accuracy can be improved by combining the predictions of multiple classifiers instead of using a single classifier, which is referred to as ensemble learning. In general, ensemble learning is a machine learning paradigm where multiple learners are trained to solve the same problem. In contrast to ordinary machine learning approaches which try to learn one hypothesis from training data, ensemble methods try to construct a set of hypotheses and combine them to use. The generalization ability of an ensemble is usually much stronger than that of base learners. The idea of ensemble learning is being successfully incorporated into various machine learning algorithms. Empirically, ensembles tend to yield better results when there is a significant diversity among the models employed. In this talk, we would like to demonstrate

How evolutionary algorithms can help improve the performance of ensemble approaches
How ensemble learning can improve the performance of evolutionary algorithms

Keyword: Ensemble Learning, No Free Lunch Theorem, Machine Learning

BIO

Rammohan Mallipeddi received the B.Tech. degree in electrical and electronics engineering from Acharya Nagarjuna University, Guntur, Andhra Pradesh, India, in 2005, and the Master’s degree in computer control and automation from the School of Electrical and Electronics Engineering, Nanyang Technological University, Singapore, in 2007. He received his Ph.D. from School of Electrical and Electronics Engineering, Nanyang Technological University, Singapore in 2010. He is currently an Assistant Professor at School of Electronics Engineering, Kyungpook National University, Taegu, South Korea. His research interests include evolutionary computation, artiﬁcial intelligence, image processing, digital signal processing, robotics and control engineering.