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Abstract: Partition nets provide a fast method for learning sensorimotor mappings. They combine the generalizing power of neural networks with the "one shot" learning of instance-based search algorithms. Partition nets adjust receptive fields and allocate network weights "on the fly," in response to the local sample density and complexity of the mapping. They can help a robot adapt quickly to bodily and environmental changes. Thus, they satisfy several important criteria for a cognitive theory of... (Update)
Context of citations to this paper: More
...easily accumulate, we need an algorithm that can combine the strengths of closest point algorithms and neural networks. Partition nets [10] are an efficient on line learning algorithm that can make fast predictions about well practised movements while quickly adapting to...
...easily accumulate, we need an algorithm that can combine the strengths of closest point algorithms and neural networks. Partition nets [17] are an efficient on line learning algorithm that can make fast predictions about well practised movements while quickly adapting to changes...
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BibTeX entry: (Update)
MacDorman, K. F. (1999). Partition Nets: An efficient on-line learning algorithm. icar 99: Ninth International Conference on Advanced Robotics, Tokyo. http://citeseer.ist.psu.edu/macdorman99partition.html More
@misc{ macdorman99partition,
author = "K. MacDorman",
title = "Partition Nets: An efficient on-line learning algorithm",
text = "MacDorman, K. F. (1999). Partition Nets: An efficient on-line learning
algorithm. icar 99: Ninth International Conference on Advanced Robotics,
Tokyo.",
year = "1999",
url = "citeseer.ist.psu.edu/macdorman99partition.html" }
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Documents on the same site (http://robotics.me.es.osaka-u.ac.jp/~kfm/): More
Grounding Symbols through Sensorimotor Integration - MacDorman (1998) (Correct)
Heuristics for Projecting a Sensorimotor Mapping - Karl Macdorman (Correct)
Partition Nets: An Efficient On-Line Learning Algorithm - MacDorman (1999) (Correct)
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