Open AccessBook
Symbolic visual learning
Katsushi Ikeuchi,Manuela Veloso +1 more
- 01 May 1997
- pp 355-355
74
TL;DR: 1. The Visual Learning Problem, 2. Multi-HASH: Learning Object Attributes and Hash Tables for Fast 3D Object Recognition, and 3. Explanation Based Learning for Mobile Robot Perception.
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Abstract: 1. The Visual Learning Problem 2. MULTI-HASH: Learning Object Attributes and Hash Tables for Fast 3D Object Recognition 3. Learning Control Strategies for Object Recognition 4. PADO: A New Learning Architecture for Object Recognition 5. Learning Organization Hierarchies of Large Modelbases for Fast Recognition 6. Application of Machine Learning in Function-Based Recognition 7. Learning a Visual Model and an Image Processing Strategy from a Series of Silhouette Images on MIRACLE-IV 8. Assembly Plan from Observation 9. Visual Event Perception 10. A Knowledge Framework for Seeing and Learning 11. Explanation Based Learning for Mobile Robot Perception 12. Navigation with Landmarks: Computing Goal Locations from Place Codes
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Citations
A model of hippocampally dependent navigation, using the temporal difference learning rule.
TL;DR: The model successfully captures gradual acquisition in both tasks, and, in particular, the ultimate development of one‐trial learning in the delayed matching‐to‐place task.
Cognitive Maps Beyond the Hippocampus
TL;DR: A conceptual framework for the role of the hippocampus and its afferent and efferent structures in rodent navigation is presented and a number of practical experiments are suggested that could support or refute it.
Theory of rodent navigation based on interacting representations of space
TL;DR: A computational theory of navigation in rodents based on interacting representations of place, head direction, and local view is presented and predictions that are testable with current technologies are generated.
268
Neuronal computations underlying the firing of place cells and their role in navigation
Neil Burgess,John O'Keefe +1 more
TL;DR: The model provides a candidate mechanism, at the level of individual cells, by which place cell information concerning self‐localization could be used to guide navigation to previously visited reward sites, and embodies specific predictions regarding the formation of place fields, the phase coding of place cell firing with respect to the hippocampal theta Rhythm.
267
A survey of semantic methods in genetic programming
TL;DR: This survey analyzes and discusses the state of the art in the field, organizing the existing methods into different categories, and restricts itself to studies where semantics is intended as the set of output values of a program on the training data.
197
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