What is it about?
Recent developments in case-based reasoning system (CBR) have led to an interest in favoring machine learning (ML) approaches as a replacement for traditional weighted distance methods. However, valuable information obtained through a training process was relinquished as transferring to other phases. This paper proposed a complete pipeline integration of CBR using kernel method designated with support vector machine (SVM) as the main engine. Since the system requires learning SVM model to be invoked in every phase, the online learning mechanism is nominated to effectively update the model when a new case adjoins. The proposed full SVM-CBR integration has been successfully built into a pipe defect detection. The achieved result indicates a substantial improvement by transferring learning information accurately.
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Why is it important?
Recent developments in case-based reasoning system (CBR) have led to an interest in favoring machine learning (ML) approaches as a replacement for traditional weighted distance methods. However, valuable information obtained through a training process was relinquished as transferring to other phases.
Perspectives
This paper proposed a complete pipeline integration of CBR using kernel method designated with support vector machine (SVM) as the main engine. Since the system requires learning SVM model to be invoked in every phase, the online learning mechanism is nominated to effectively update the model when a new case adjoins. The proposed full SVM-CBR integration has been successfully built into a pipe defect detection. The achieved result indicates a substantial improvement by transferring learning information accurately.
Dr zhiyuan chen
The University of Nottingham Malaysia
Read the Original
This page is a summary of: A complete online-SVM pipeline for case-based reasoning system: a study on pipe defect detection system, Soft Computing, May 2020, Springer Science + Business Media,
DOI: 10.1007/s00500-020-04985-7.
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