Perspectives of Neural-Symbolic Integration
The human brain possesses the remarkable capability of understanding, interpreting, and producing language, structures, and logic. Unlike their biological counterparts, artificial neural networks do not form such a close liason with symbolic reasoning: logic-based inference mechanisms and statistica...
| Συγγραφή απο Οργανισμό/Αρχή: | |
|---|---|
| Άλλοι συγγραφείς: | , |
| Μορφή: | Ηλεκτρονική πηγή Ηλ. βιβλίο |
| Γλώσσα: | English |
| Έκδοση: |
Berlin, Heidelberg :
Springer Berlin Heidelberg,
2007.
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| Σειρά: | Studies in Computational Intelligence,
77 |
| Θέματα: | |
| Διαθέσιμο Online: | Full Text via HEAL-Link |
Πίνακας περιεχομένων:
- 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.
