NDLI: Curiosity driven incremental LDA agent active learning

Content Provider	IEEE Xplore Digital Library
Author	Shaoning Pang Ozawa, S. Kasabov, N.
Copyright Year	2009
Description	Author affiliation: Faculty of Engineering, Kobe University, Japan (Ozawa, S.) \|\| Knowledge Engineering & Discovery Research Institute, Auckland University of Technology, Private Bag, 92006, 1020, New Zealand (Shaoning Pang; Kasabov, N.)
Abstract	This paper presented a novel active linear discriminant analysis (LDA) learning method in the form of curiosity-driven incremental LDA (cILDA) and multiple cILDA agents cooperative learning (mcILDA). The curiosity in psychology here is modelled mathematically as a discriminability residue in-between instance space and its corresponding eigenspace. As the learning proceeds, the curiosity of an individual agent updates over time by two incremental learning processes: One updates the characterization of eigenspace and another re-calculates the curiosity. In the multi-agent scenario, individual agent communicates and cooperates with each other at every learning stage to discover the discriminant characterization of the whole pattern. In the experiment, we described how the discriminative instances could be significantly selected based on the curiosity with, at most, minor sacrifices in learning rate and classification accuracy. The experimental results show that the proposed curiosity learning performs gracefully under different level of redundancy, and the proposed cILDA/mcILDA learning system is capable of learning less instances, but has more often an improved discrimination performance.
Starting Page	2401
Ending Page	2408
File Size	4984892
Page Count	8
File Format	PDF
ISBN	9781424435487
ISSN	10987576
DOI	10.1109/IJCNN.2009.5178811
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2009-06-14
Publisher Place	USA
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Linear discriminant analysis Learning systems Machine learning algorithms Intelligent agent Neural networks Psychology Mathematical model Iterative algorithms Machine learning Knowledge engineering
Content Type	Text
Resource Type	Article

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