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Titlebook: Computational Intelligence and Intelligent Systems; 6th International Sy Zhenhua Li,Xiang Li,Zhihua Cai Conference proceedings 2012 Springe

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51#
發(fā)表于 2025-3-30 11:46:35 | 只看該作者
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發(fā)表于 2025-3-30 16:19:29 | 只看該作者
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發(fā)表于 2025-3-30 20:35:28 | 只看該作者
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發(fā)表于 2025-3-30 23:50:54 | 只看該作者
Applying Support Vector Machine to Time Series Prediction in Oracletruct SVM prediction model according to the time series data. Comparing with the traditional time series prediction model, SVM prediction models can reveal non-linear, non-stationary and randomness of the time series, and have higher prediction accuracy.
55#
發(fā)表于 2025-3-31 03:45:52 | 只看該作者
Dynamic FP-Tree Pruning for Concurrent Frequent Itemsets Miningonstruction of FP-tree and the discovery of frequent itemsets can be realized simultaneously. Compared with FP-growth, it is not necessary to mine frequent itemsets after the construction of the whole FP-tree in Dynamic-prune. Experimental results show Dynamic-prune is efficient and scalable.
56#
發(fā)表于 2025-3-31 08:59:00 | 只看該作者
MapReduce-Based Bayesian Automatic Text Classifier Used in Digital Librarynhance the overall efficiency. The new method can classify documents into different groups according to its subject. In this way, it is helpful for management and storage of information. Thus Mapreduce-based Bayesian text classifier can be used in digital library successfully which will provide better service for people.
57#
發(fā)表于 2025-3-31 11:14:30 | 只看該作者
58#
發(fā)表于 2025-3-31 13:21:23 | 只看該作者
Preferences Predictions of Learning Objects Supported by Collaborative Recommendationsceive recommendations based on outcomes of tests they have already taken. In this paper we explore methods from formal concept analysis for predicting students’ preferences with respect to learning objects going to be suggested to them before they have taken relevant tests.
59#
發(fā)表于 2025-3-31 21:29:46 | 只看該作者
60#
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