Titlebook: Database and Expert Systems Applications; 23rd International C Stephen W. Liddle,Klaus-Dieter Schewe,Xiaofang Zho Conference proceedings 20

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Combination of Machine-Learning Algorithms for Fault Prediction in High-Precision Foundriesch and we propose a general method to foresee all the defects via building a meta-classifier combining different methods and without the need for selecting the best algorithm for each defect or available data. Finally, we compare the obtained results showing that the new approach allows us to obtain

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龙卷风

faultfinder

Conference proceedings 2012d query answering; structuring, compression and optimization; failure, fault analysis, and uncertainty; predication, extraction, and annotation; ranking and personalisation; database partitioning and performance measurement; recommendation and prediction systems; business processes; social networking..

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Competencies – Longer Term Use & Developmentdex is light-weight, and the overall storage requirement for both reduced column and index is less than 135%, whereas existing DBMS technologies can require 200-400%. As a proof-of-concept, we evaluate univariate range queries that additionally return column values, a critical component of data anal

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https://doi.org/10.1007/978-1-4613-2725-7ext, and a nearest-prototype classifier, which helps to recognize instances of the topological relationships. This approach is unsupervised, so it does not need annotated data. Moreover, it is based on an ontology, which prevents the hand-crafting of . rules. Experimental results on real datasets sh

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