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Titlebook: Artificial Neural Networks - ICANN 2010; 20th International C Konstantinos Diamantaras,Wlodek Duch,Lazaros S. Il Conference proceedings 201

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Support Vector Machines-Kernel Algorithms for the Estimation of the Water Supply in Cyprushance the quality of the results and to offer an optimization approach. The final models that were produced have proven to perform with an error of very low magnitude in the testing phase when first time seen data were used.
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An Online Incremental Learning Support Vector Machine for Large-scale Data long testing time. In this paper, we propose an online incremental learning SVM for large data sets. The proposed method mainly consists of two components, Learning Prototypes (LPs) and Learning SVs (LSVs). Experimental results demonstrate that the proposed algorithm is effective for incremental learning problems and large-scale problems.
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,Good Outcomes from the , (1994–1995),d stage the selected pairs for update often appear repeatedly during the algorithm. Taking advantage of this, we shall propose a procedure to combine previously used descent directions that results in much fewer iterations in this second stage and that may also lead to noticeable savings in kernel operations.
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https://doi.org/10.1007/978-3-662-07212-7 long testing time. In this paper, we propose an online incremental learning SVM for large data sets. The proposed method mainly consists of two components, Learning Prototypes (LPs) and Learning SVs (LSVs). Experimental results demonstrate that the proposed algorithm is effective for incremental learning problems and large-scale problems.
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https://doi.org/10.1007/978-3-658-34481-8ns. In the metaphor evaluation process, the candidate nouns are evaluated based on the similarities between the meanings of metaphors including the candidate nouns and the meaning of the input expression.
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