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|Title:||Towards general semi-supervised clustering using a cognitive reinforcement K-Iteration fast learning artificial neural network (R-Kflann)||Authors:||Tse, Rina||Keywords:||DRNTU::Engineering::Computer science and engineering::Computing methodologies::Artificial intelligence||Issue Date:||2010||Source:||Tse, R. (2010).Towards general semi-supervised clustering using a cognitive reinforcement K-Iteration fast learning artificial neural network (R-Kflann). Master’s thesis, Nanyang Technological University, Singapore.||Abstract:||The study of a semi-supervised clustering has recently attracted great interest from the data clustering community. Work in semi-supervised clustering systems has been done focusing on specific types of auxiliary information, i.e. partial labeling or pairwise constraints. However, in some applications the clustering characteristics desired may not be restricted to only these predefined types of constraints. Furthermore, the user may not always be able to formulate an explicit clustering specification. In such cases, only a weak good or bad evaluation feedback is obtainable as semi-supervision information. This research proposes a novel form of semi-supervised clustering using Reinforcement K-Iteration Fast Learning Artificial Neural Network (R-KFLANN) architecture that utilizes generic reward or punishment feedback, enabling it to address different types of high-level clustering requirements provided at run-time. To illustrate this concept, the R-KFLANN was tested in two different application domains of data classification and robot mapping. The results indicate that the system was able to adapt to the online reinforcement presented and eventually improve the output in serving the covert specifications of both tasks. It could significantly improve the clusters using only overall failure rate information loosely coupled with the hidden class labels in the classification problem. This was also evident in a navigation problem; when the clustering specification could not even be explicitly formulated, R-KFLANN was still able to incorporate the high-level task’s characteristics into the cluster representation, yielding a better map efficiency. Additionally, it could fulfill the navigation task requirements which would not be achievable unless the system was tuned manually using a small tolerance. These findings suggest the usefulness of R-KFLANN semi-supervised clustering in serving clustering objectives imposed online by the high-level tasks without being restricted to the traditional semi-supervised constraints.||URI:||https://hdl.handle.net/10356/41504||DOI:||10.32657/10356/41504||Fulltext Permission:||open||Fulltext Availability:||With Fulltext|
|Appears in Collections:||SCSE Theses|
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Updated on May 11, 2021
Updated on May 11, 2021
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