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Titlebook: Intelligent Data Engineering and Automated Learning -- IDEAL 2010; 11th International C Colin Fyfe,Peter Tino,Hujun Yin Conference proceedi

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51#
發(fā)表于 2025-3-30 10:45:22 | 只看該作者
Large Scale Instance Selection by Means of a Parallel Algorithm,ecially efficient when we use instance selection algorithms that are of a high computational cost..An extensive comparison in 35 datasets of medium and large sizes from the UCI Machine Learning Repository shows the usefulness of our method. Additionally, the method is applied to 6 huge datasets (fro
52#
發(fā)表于 2025-3-30 15:48:00 | 只看該作者
53#
發(fā)表于 2025-3-30 20:31:52 | 只看該作者
Cost Optimization of a Localized Irrigation System Using Genetic Algorithms,lgorithm (GA-LCLI) to search a less costly localized irrigation project. The results are compared with those presented by a previous work: there is an improvement in the execution runtime and in the cost of the irrigation systems.
54#
發(fā)表于 2025-3-30 21:34:32 | 只看該作者
55#
發(fā)表于 2025-3-31 01:30:12 | 只看該作者
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發(fā)表于 2025-3-31 07:45:57 | 只看該作者
Conference proceedings 2010nteract with each other and with leading academics and industries in the areas of machine learning, information processing, data mining, knowledge management, bio-informatics, neu- informatics, bio-inspired models, agents and distributed systems, and hybrid systems. This volume contains the papers p
57#
發(fā)表于 2025-3-31 12:24:03 | 只看該作者
58#
發(fā)表于 2025-3-31 16:46:35 | 只看該作者
59#
發(fā)表于 2025-3-31 18:39:11 | 只看該作者
Dimension Reduction for Regression with Bottleneck Neural Networks,Regression (COIR), which has been reported to perform well compared to many other DRR methods. The bottleneck network compares favorably with COIR: it is applicable to larger data sets, it is less sensitive to tuning parameters and it gives better results on several real data sets.
60#
發(fā)表于 2025-3-31 23:06:06 | 只看該作者
Gallbladder Boundary Segmentation from Ultrasound Images Using Active Contour Model,approximate shape of the gallbladder was found by applying the motion equation model. The tests conducted have shown that for the 220 US images of the gallbladder, the area error rate (AER) amounted to 18.15%.
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