Fundamentals and Future Possibilities in Hybrid Learning

Author

Donald C. Wunsch, Missouri University of Science and TechnologyFollow

Abstract

Neural networks and related technologies have proven to be transformational technologies. Various technological advances have enabled breakthroughs beyond what many would have predicted when the field's popularity was on the rise. However, many challenges remain. Some of the field's progress will be described while key future challenges and approaches to them will be outlined. Of particular promise are approaches that offer hybrids of previously successful methods in novel combinations.

Recommended Citation

D. C. Wunsch, "Fundamentals and Future Possibilities in Hybrid Learning," University of the District of Columbia, Feb 2016.

Meeting Name

2016 IEEE CIS Winter School on Big Data in Computational Intelligence: From Fundamental Principles to Complex System Applications (2016: Feb. 19-21, Washington, DC)

Department(s)

Electrical and Computer Engineering

Research Center/Lab(s)

Center for High Performance Computing Research

Second Research Center/Lab

Intelligent Systems Center

Comments

Plenary Talk #1, Day 2 of the 2016 IEEE Computational Intelligence Society Winter School

Document Type

Article - Conference proceedings

Document Version

Citation

File Type

text

Language(s)

English

Rights

© 2016 University of the District of Columbia, All rights reserved.

Publication Date

01 Feb 2016