Intellectual issues in the history of artificial intelligence
The study of information: interdisciplinary messages
Parallel distributed processing: explorations in the microstructure of cognition, vol. 2: psychological and biological models
Computing with structured connectionist networks
Communications of the ACM
Connectionist models and their implications: readings from cognitive science
Connectionist models and their implications: readings from cognitive science
Learning in structured connectionist networks
Learning in structured connectionist networks
Connectionist learning procedures
Artificial Intelligence
Parallel distributed processing: explorations in the microstructure of cognition, vol. 1: foundations
Semantic Networks: An Evidential Formalization and Its Connectionist Realization
Semantic Networks: An Evidential Formalization and Its Connectionist Realization
Artificial Neural Networks: A Tutorial
Computer - Special issue: neural computing: companion issue to Spring 1996 IEEE Computational Science & Engineering
A Graduated Assignment Algorithm for Graph Matching
IEEE Transactions on Pattern Analysis and Machine Intelligence
Optimization in model matching and perceptual organization
Neural Computation
Selective attention and the acquisition of spatial semantics
NeMLaP3/CoNLL '98 Proceedings of the Joint Conferences on New Methods in Language Processing and Computational Natural Language Learning
| Hi-index | 4.10 |
The authors are concerned with how one can design, realize, and analyze networks that embody the specific computational structures needed to solve hard problems. They focus on the design and use of massively parallel connectionist computational models, particularly in artificial intelligence. They describe a computing environment for working with structured networks and present some sample applications. Throughout, they treat adaptation and learning as ways to improve structured networks, not as replacements for analysis and design.