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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| Other Authors: | |
| Format: | Student Research Paper |
| Language: | English |
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2015
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| Online Access: | https://hdl.handle.net/10356/105728 http://hdl.handle.net/10220/26034 |
| _version_ | 1826126794550411264 |
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| author | Ruan, Pingcheng |
| author2 | Suresh Sundaram |
| author_facet | Suresh Sundaram Ruan, Pingcheng |
| author_sort | Ruan, Pingcheng |
| collection | NTU |
| 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. |
| first_indexed | 2024-10-01T06:58:09Z |
| format | Student Research Paper |
| id | ntu-10356/105728 |
| institution | Nanyang Technological University |
| language | English |
| last_indexed | 2024-10-01T06:58:09Z |
| publishDate | 2015 |
| record_format | dspace |
| spelling | ntu-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 |
| spellingShingle | DRNTU::Engineering::Computer science and engineering Ruan, Pingcheng Modeling meta-cognition for efficient learning |
| title | 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_short | Modeling meta-cognition for efficient learning |
| title_sort | modeling meta cognition for efficient learning |
| topic | DRNTU::Engineering::Computer science and engineering |
| url | https://hdl.handle.net/10356/105728 http://hdl.handle.net/10220/26034 |
| work_keys_str_mv | AT ruanpingcheng modelingmetacognitionforefficientlearning |