Fundamentals and Future Possibilities in Hybrid Learning
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.
D. C. Wunsch, "Fundamentals and Future Possibilities in Hybrid Learning," University of the District of Columbia, Feb 2016.
2016 IEEE CIS Winter School on Big Data in Computational Intelligence: From Fundamental Principles to Complex System Applications (2016: Feb. 19-21, Washington, DC)
Electrical and Computer Engineering
Center for High Performance Computing Research
Second Research Center/Lab
Intelligent Systems Center
Article - Conference proceedings
© 2016 University of the District of Columbia, All rights reserved.
01 Feb 2016