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On modeling MapReduce with granular computing
2011 IEEE International Conference on Granular Computing, 2011Cloud computing focuses on supporting high scalable and high available parallel and distributed computing, based on the infrastructure built on top of large scale clusters which contain a large number of cheap PC servers, to process the huge amounts of data generated by Internet.
Bo Zhang, Zhongzhi Shi, Bo Zhang
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Perception Learning as Granular Computing
2008 Fourth International Conference on Natural Computation, 2008Zadeh proposed that there are three basic concepts that underlie human cognition: granulation, organization and causation and a granule being a clump of points (objects) drawn together by indistinguishability, similarity, proximity or functionality.
Hong Hu 0001, Zhongzhi Shi
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GBSVM: An Efficient and Robust Support Vector Machine Framework via Granular-Ball Computing
IEEE Transactions on Neural Networks and Learning SystemsGranular-ball support vector machine (GBSVM) is a significant attempt to construct a classifier using the coarse-to-fine granularity of a granular ball as input, rather than a single data point.
Shuyin Xia +5 more
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Granular modelling of signals: A framework of Granular Computing
Information Sciences, 2013In spite of the evident diversity of models of signals and time series, there is still an urgent need to develop constructs that are both accurate and highly interpretable (human-centric). While a great deal of research has been devoted to the design of nonlinear models of time series (with anticipation of achieving high accuracy of prediction), an ...
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GB-RVFL: Fusion of Randomized Neural Network and Granular Ball Computing
Pattern RecognitionThe random vector functional link (RVFL) network is a prominent classification model with strong generalization ability. However, RVFL treats all samples uniformly, ignoring whether they are pure or noisy, and its scalability is limited due to the need ...
M. Sajid, A. Quadir, M. Tanveer
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Algebraic approaches to granular computing
Granular Computing, 2019Mengjun Hu, Yiyu Yao, Mao Hua
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Granular Computing and Computational Complexity
2010Granular computing is to imitate humans multigranular computing strategy to problem solving in order to endow computers with the same capability. Its final goal is to reduce the computational complexity. To the end, based on the simplicity principle the problem at hand should be represented as simpler as possible.
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GUHA method and granular computing
2005 IEEE International Conference on Granular Computing, 2005GUHA method of exploratory data analysis is presented. GUHA offers all interesting facts following from the analysed data to the given problem. Its development started about 40 years ago. It is implemented in the form of GUHA-procedures. Implementation techniques called now granular computing are used. The software system LISp-Miner containing six GUHA
Jan Rauch, Milan Simunek
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2007
The current research in granular computing is dominated by set-theoretic models such as rough sets and fuzzy sets. By recasting the existing studies in a wider context, we propose a unified framework of granular computing. The new framework extends results obtained in the set-theoretic setting and extracts high-level common principles from a wide range
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The current research in granular computing is dominated by set-theoretic models such as rough sets and fuzzy sets. By recasting the existing studies in a wider context, we propose a unified framework of granular computing. The new framework extends results obtained in the set-theoretic setting and extracts high-level common principles from a wide range
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Granular computing-based deep learning for text classification
Information Sciences, 2023Rashid Behzadidoost +2 more
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