Modeling meta-cognition for efficient learning

‘Meta-cognitive Radial Basis Function Network’ (McRBFN) and ‘Projection Based Learning’ (PBL) is a machine-learning algorithm used to classify a data sample. Its meta-cognitive component selects one learning strategy from sample deletion, neuron growth and parameter update and sample reservation. Th...

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Main Author:	Ruan, Pingcheng
Other Authors:	Suresh Sundaram
Format:	Student Research Paper
Language:	English
Published:	2015
Subjects:	DRNTU::Engineering::Computer science and engineering
Online Access:	https://hdl.handle.net/10356/105728 http://hdl.handle.net/10220/26034
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Institution:	Nanyang Technological University
Language:	English

id	sg-ntu-dr.10356-105728
record_format	dspace
spelling	sg-ntu-dr.10356-1057282020-09-27T20:27:09Z Modeling meta-cognition for efficient learning Ruan, Pingcheng Suresh Sundaram School of Computer Engineering DRNTU::Engineering::Computer science and engineering ‘Meta-cognitive Radial Basis Function Network’ (McRBFN) and ‘Projection Based Learning’ (PBL) is a machine-learning algorithm used to classify a data sample. Its meta-cognitive component selects one learning strategy from sample deletion, neuron growth and parameter update and sample reservation. The cognitive component adjusts the output weight to minimize the error of prediction using PBL algorithm. In this paper, we propose an improvement on the sample addition strategy in order to prevent the corruption of existing knowledge. At last, we evaluate the improved algorithm using three benchmarking classification problems. 2015-06-23T07:30:33Z 2019-12-06T21:56:46Z 2015-06-23T07:30:33Z 2019-12-06T21:56:46Z 2014 2014 Student Research Paper Ruan, P. (2014). Modeling meta-cognition for efficient learning. Student research paper, Nanyang Technological University. https://hdl.handle.net/10356/105728 http://hdl.handle.net/10220/26034 en © 2014 The Author(s). 4 p. application/pdf
institution	Nanyang Technological University
building	NTU Library
country	Singapore
collection	DR-NTU
language	English
topic	DRNTU::Engineering::Computer science and engineering
spellingShingle	DRNTU::Engineering::Computer science and engineering Ruan, Pingcheng Modeling meta-cognition for efficient learning
description	‘Meta-cognitive Radial Basis Function Network’ (McRBFN) and ‘Projection Based Learning’ (PBL) is a machine-learning algorithm used to classify a data sample. Its meta-cognitive component selects one learning strategy from sample deletion, neuron growth and parameter update and sample reservation. The cognitive component adjusts the output weight to minimize the error of prediction using PBL algorithm. In this paper, we propose an improvement on the sample addition strategy in order to prevent the corruption of existing knowledge. At last, we evaluate the improved algorithm using three benchmarking classification problems.
author2	Suresh Sundaram
author_facet	Suresh Sundaram Ruan, Pingcheng
format	Student Research Paper
author	Ruan, Pingcheng
author_sort	Ruan, Pingcheng
title	Modeling meta-cognition for efficient learning
title_short	Modeling meta-cognition for efficient learning
title_full	Modeling meta-cognition for efficient learning
title_fullStr	Modeling meta-cognition for efficient learning
title_full_unstemmed	Modeling meta-cognition for efficient learning
title_sort	modeling meta-cognition for efficient learning
publishDate	2015
url	https://hdl.handle.net/10356/105728 http://hdl.handle.net/10220/26034
_version_	1681058718979981312

Modeling meta-cognition for efficient learning

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