Perspectives of neural-symbolic integration
著者
書誌事項
Perspectives of neural-symbolic integration
（Studies in computational intelligence, v. 77）
Springer, c2007
大学図書館所蔵 全2件
注記
Includes bibliographical references and index
内容説明・目次
内容説明
When it comes to robotics and bioinformatics, the Holy Grail everyone is seeking is how to dovetail logic-based inference and statistical machine learning. This volume offers some possible solutions to this eternal problem. Edited with flair and sensitivity by Hammer and Hitzler, the book contains state-of-the-art contributions in neural-symbolic integration, covering `loose' coupling by means of structure kernels or recursive models as well as `strong' coupling of logic and neural networks.
目次
Structured Data and Neural Networks.- Kernels for Strings and Graphs.- Comparing Sequence Classification Algorithms for Protein Subcellular Localization.- Mining Structure-Activity Relations in Biological Neural Networks using NeuronRank.- Adaptive Contextual Processing of Structured Data by Recursive Neural Networks: A Survey of Computational Properties.- Markovian Bias of Neural-based Architectures With Feedback Connections.- Time Series Prediction with the Self-Organizing Map: A Review.- A Dual Interaction Perspective for Robot Cognition: Grasping as a "Rosetta Stone".- Logic and Neural Networks.- SHRUTI: A Neurally Motivated Architecture for Rapid, Scalable Inference.- The Core Method: Connectionist Model Generation for First-Order Logic Programs.- Learning Models of Predicate Logical Theories with Neural Networks Based on Topos Theory.- Advances in Neural-Symbolic Learning Systems: Modal and Temporal Reasoning.- Connectionist Representation of Multi-Valued Logic Programs.
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