Synopsis
Traditional artificial intelligence and neural networks are generally considered appropriate for solving different types of problems. On the surface, these two approaches appear to be very different, but a growing body of current research is focused on how the strengths of each can be incorporated into the other and built into systems that include the best features of both.
Artificial Intelligence and Neural Networks: Steps Toward Principled Integration is a critical examination of the key issues, underlying assumptions, and suggestions related to the reconciliation and principled integration of artificial intelligence and neural networks. With contributions from leading researchers in the field, this comprehensive text provides a thorough introduction to the basics of symbol processing, connectionist networks, and their integration. Numerous examples of the integration of artificial intelligence and neural networks for a variety of specific applications provide unique insight into this evolving area.
Includes contributions from some of the leading researchers in this area
Provides a complete introduction to the basics of symbol processing, connectionist networks, and their integration
Includes examples of the integration of artificial intelligence and neural networks for a variety of specific applications, including vision and pattern recognition
About the Author
Vasant Honavar is Professor of Information Sciences and Technology and of Computer Science at the Pennsylvania State University where he holds the Edward Frymoyer Endowed Chair, and heads the Artificial Intelligence Research Laboratory and the Center for Big Data Analytics and Discovery Informatics. He received his PhD specializing in Artificial Intelligence from the University of Wisconsin at Madison in 1990. Honavar's current research and teaching interests include Artificial Intelligence, Machine Learning, Bioinformatics, Big Data Analytics, Discovery Informatics, Social Informatics, Security Informatics, and Health Informatics. Honavar has led research projects funded by National Science Foundation, the National Institutes of Health, the United States Department of Agriculture, and the Department of Defense that have resulted in foundational research contributions (documented in over 250 peer-reviewed publications) in Scalable approaches to building predictive models from large, distributed, semantically disparate data (big data); Constructing predictive models from sequence, image, text, multi-relational, graph-structured data; Eliciting causal information from multiple sources of observational and experimental data; Selective sharing of knowledge across disparate knowledge bases; Representing and reasoning about preferences; Composing complex services from components; and Applications in Bioinformatics, Social network Informatics, Health Informatics, Energy Informatics, Security Informatics.
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