| Description |
1 online resource |
| Series |
Studies in computational intelligence ; volume 910 |
|
Studies in computational intelligence ; v. 910.
|
| Contents |
Introduction -- The Gap between Symbolic and Connectionist Approaches -- Spatializing Symbolic Structures for the Gap -- The Criteria, Challenges, and the Back-Propagation Method -- Design Principles of Geometric Connectionist Machines -- A Geometric Connectionist Machine for Word-Senses -- Geometric Connectionist Machines for Triple Classification -- Conclusions & Outlooks |
| Summary |
The unification of symbolist and connectionist models is a major trend in AI. The key is to keep the symbolic semantics unchanged. Unfortunately, present embedding approaches cannot. The approach in this book makes the unification possible. It is indeed a new and promising approach in AI.-Bo Zhang, Director of AI Institute, Tsinghua It is indeed wonderful to see the reviving of the important theme Nural Symbolic Model. Given the popularity and prevalence of deep learning, symbolic processing is often neglected or downplayed. This book confronts this old issue head on, with a historical look, incorporating recent advances and new perspectives, thus leading to promising new methods and approaches.-Ron Sun (RPI), on Governing Board of Cognitive Science Society Both for language and humor, approaches like those described in this book are the way to snickerdoodle wombats.-Christian F. Hempelmann (Texas A & M-Commerce) on Executive Board of International Society for Humor Studies |
| Bibliography |
Includes bibliographical references and index |
| Notes |
Online resource; title from digital title page (viewed on October 12, 2020) |
| Subject |
Neural networks (Computer science)
|
|
Logic, Symbolic and mathematical.
|
|
Logic, Symbolic and mathematical
|
|
Neural networks (Computer science)
|
| Form |
Electronic book
|
| ISBN |
9783030562755 |
|
3030562751 |
|
9783030562762 |
|
303056276X |
|
9783030562779 |
|
3030562778 |
|
