Titlebook: Innovation Policy in the Knowledge-Based Economy; Maryann P. Feldman,Albert N. Link Book 2001 Springer Science+Business Media New York 200

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Maryann P. Feldman,Albert N. Linkhm which automatically learns the weights of the rules for classification during inference. We use the p-value obtained from Fisher’s exact test to determine the statistical significance of rules. The machine learning-based classifiers like Neural Network, SVM and rule-based classifiers like RIPPER

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ns the structure alternatives with their variations and common points. This feature model satisfies document-oriented constraints so as user constraints reflecting good practices or particular needs. It leads to a set of data structuring alternatives to be considered by the user for his operational

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John E. Jankowskirplay between incomplete data and recommended visual analytics, under a combination of different conditions including: (1) the distribution of incomplete data, (2) the adopted data imputation methods, (3) the types of insights revealed by recommended visualizations, and (4) the quality measures used

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Condyle

John A. Hansenocess. We focus on Spark and show design and implementation of the proposed methods for Spark SQL. We verify by the experiments that our optimization methods really contribute to the reduction of the processing time of Spark SQL queries including row pattern matching.

GRAZE

Andrew C. Brod,Albert N. Linkarch results quality through long-range edges connecting pivots of different partitions. We present experimental results using real datasets that show that . applied to the . graph was able to decrease the construction time while increasing the approximate search recall when compared to the .. Regar

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William Zumeta,Joyce.S. Ravelingd vector embedding to incorporate semantic oppositeness, so that an effective mapping of semantic oppositeness can be obtained in a given vector space. In the experiments we present in this paper, we show that our proposed method achieves a training accuracy of 97.91% and a test accuracy of 97.82%,

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ts of log data generated in different industrial applications. The experimental results show that Lopper achieves the accuracy with 92.26% on average which is much better than comparative methods and remains high efficiency at the same time. We also conduct experiments on system anomaly detection ta

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Richard R. Nelsontional probability indicate a high risk of de-anonymisation, similar to quasi-identifiers in syntactic anonymisation schemes, and can be separated instead of deleted. Attribute compartmentation removes the risk of privacy exposure, and deletion avoidance results in a significant reduction in informa

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