NDLI: Similarity learning based on extension logic

Content Provider	IEEE Xplore Digital Library
Author	Bin He Xuefeng Zhu
Copyright Year	2004
Description	Author affiliation: Coll. of Autom. Sci. & Eng., South China Univ. of Technol., Guangzhou, China (Bin He; Xuefeng Zhu)
Abstract	Based on extension logic, this paper presents a novel learning method-similarity learning method. Similarity learning is a kind of learning driven by domain knowledge. The goal is to solve incompatible problems. It starts from the key characteristics of the goal and condition of original problems. During similarity learning, the extensibility is analyzed first, and then similarity goals of the original goals and corresponding similarity condition of original condition are considered. Finally, similarity transformations based on the principles of similarity transformations are made and thus the feasible satisfactory similarity solutions for similarity problems constitute similarity solutions for the original problems. Similarity learning has also a tradeoff between exploration and exploitation. The search process of similarity objects and similarity transformations is both a kind of trial-and-error search and data mining process. It differentiates from reinforcement learning in that it is expanded based on similarity biases and not on probability biases.
Sponsorship	Zhejian Univ. Nat. Lab. of Ind. Control Technol. IEEE Robotics and Automation Soc. Nat. Natural Sci. Found. of China Chinese Assoc. of Automation
Starting Page	1900
Ending Page	1904
File Size	274777
Page Count	5
File Format	PDF
ISBN	0780382730
DOI	10.1109/WCICA.2004.1341909
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2004-06-15
Publisher Place	China
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Logic Marine animals Learning systems Helium Educational institutions Automation Data mining Airplanes Birds Underwater vehicles
Content Type	Text
Resource Type	Article

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