Development of an intelligent system using Kernel-based learning methods for predicting oil-palm yield.
Intelligent systems based on machine learning techniques, such as classification, clustering, are gaining wide spread popularity in real world applications. This paper presents our work on developing an intelligent system for predicting crop yield, for example oil-palm yield, from climate and planta...
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| Format: | Article |
| Language: | English |
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Penerbit UTM Press
2005
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| Online Access: | http://eprints.utm.my/8530/ http://eprints.utm.my/8530/1/MohdNoorMdSap2005_DevelopmentOfAnIntelligentOfSystemUsingKernel.PDF |
| _version_ | 1848891707864645632 |
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| author | Md. Sap, Mohd. Noor Awan, A. Majid |
| author_facet | Md. Sap, Mohd. Noor Awan, A. Majid |
| author_sort | Md. Sap, Mohd. Noor |
| building | UTeM Institutional Repository |
| collection | Online Access |
| description | Intelligent systems based on machine learning techniques, such as classification, clustering, are gaining wide spread popularity in real world applications. This paper presents our work on developing an intelligent system for predicting crop yield, for example oil-palm yield, from climate and plantation data. At the core of our system is a method for unsupervised partitioning of data for finding spatio-temporal patterns in climate data using kernel methods which offer strength to deal with complex data non-linearly separable in input space. This work gets inspiration from the notion that a non-linear data transformation into some high dimensional feature space increases the possibility of linear separability of the patterns in the transformed space. Therefore, it simplifies exploration of the associated structure in the data. Kernel methods implicitly perform a non-linear mapping of the input data into a high dimensional feature space by replacing the inner products with an appropriate positive definite function. In this paper we present a robust weighted kernel k-means algorithm incorporating spatial constraints for clustering climate data. The proposed algorithm can effectively handle noise, outliers and auto-correlation in the spatial data, for effective and efficient data analysis by exploring patterns and structures in the data, and thus can be used for predicting oil-palm yield by analyzing various factors affecting oil-palm yield. |
| first_indexed | 2025-11-15T21:02:15Z |
| format | Article |
| id | utm-8530 |
| institution | Universiti Teknologi Malaysia |
| institution_category | Local University |
| language | English |
| last_indexed | 2025-11-15T21:02:15Z |
| publishDate | 2005 |
| publisher | Penerbit UTM Press |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | utm-85302017-11-01T04:17:34Z http://eprints.utm.my/8530/ Development of an intelligent system using Kernel-based learning methods for predicting oil-palm yield. Md. Sap, Mohd. Noor Awan, A. Majid SB Plant culture QA76 Computer software Intelligent systems based on machine learning techniques, such as classification, clustering, are gaining wide spread popularity in real world applications. This paper presents our work on developing an intelligent system for predicting crop yield, for example oil-palm yield, from climate and plantation data. At the core of our system is a method for unsupervised partitioning of data for finding spatio-temporal patterns in climate data using kernel methods which offer strength to deal with complex data non-linearly separable in input space. This work gets inspiration from the notion that a non-linear data transformation into some high dimensional feature space increases the possibility of linear separability of the patterns in the transformed space. Therefore, it simplifies exploration of the associated structure in the data. Kernel methods implicitly perform a non-linear mapping of the input data into a high dimensional feature space by replacing the inner products with an appropriate positive definite function. In this paper we present a robust weighted kernel k-means algorithm incorporating spatial constraints for clustering climate data. The proposed algorithm can effectively handle noise, outliers and auto-correlation in the spatial data, for effective and efficient data analysis by exploring patterns and structures in the data, and thus can be used for predicting oil-palm yield by analyzing various factors affecting oil-palm yield. Penerbit UTM Press 2005-06 Article PeerReviewed application/pdf en http://eprints.utm.my/8530/1/MohdNoorMdSap2005_DevelopmentOfAnIntelligentOfSystemUsingKernel.PDF Md. Sap, Mohd. Noor and Awan, A. Majid (2005) Development of an intelligent system using Kernel-based learning methods for predicting oil-palm yield. Jurnal Teknologi Maklumat, 17 (1). pp. 66-77. ISSN 0128-3790 |
| spellingShingle | SB Plant culture QA76 Computer software Md. Sap, Mohd. Noor Awan, A. Majid Development of an intelligent system using Kernel-based learning methods for predicting oil-palm yield. |
| title | Development of an intelligent system using Kernel-based learning methods for predicting oil-palm yield. |
| title_full | Development of an intelligent system using Kernel-based learning methods for predicting oil-palm yield. |
| title_fullStr | Development of an intelligent system using Kernel-based learning methods for predicting oil-palm yield. |
| title_full_unstemmed | Development of an intelligent system using Kernel-based learning methods for predicting oil-palm yield. |
| title_short | Development of an intelligent system using Kernel-based learning methods for predicting oil-palm yield. |
| title_sort | development of an intelligent system using kernel-based learning methods for predicting oil-palm yield. |
| topic | SB Plant culture QA76 Computer software |
| url | http://eprints.utm.my/8530/ http://eprints.utm.my/8530/1/MohdNoorMdSap2005_DevelopmentOfAnIntelligentOfSystemUsingKernel.PDF |