Titlebook: Knowledge Discovery, Knowledge Engineering and Knowledge Management; 11th International J Ana Fred,Ana Salgado,Joaquim Filipe Conference pr

Magnanimous

书目名称Knowledge Discovery, Knowledge Engineering and Knowledge Management影响因子(影响力)




书目名称Knowledge Discovery, Knowledge Engineering and Knowledge Management影响因子(影响力)学科排名




书目名称Knowledge Discovery, Knowledge Engineering and Knowledge Management网络公开度




书目名称Knowledge Discovery, Knowledge Engineering and Knowledge Management网络公开度学科排名




书目名称Knowledge Discovery, Knowledge Engineering and Knowledge Management被引频次




书目名称Knowledge Discovery, Knowledge Engineering and Knowledge Management被引频次学科排名




书目名称Knowledge Discovery, Knowledge Engineering and Knowledge Management年度引用




书目名称Knowledge Discovery, Knowledge Engineering and Knowledge Management年度引用学科排名




书目名称Knowledge Discovery, Knowledge Engineering and Knowledge Management读者反馈




书目名称Knowledge Discovery, Knowledge Engineering and Knowledge Management读者反馈学科排名

transplantation

强制性

978-3-030-66195-3Springer Nature Switzerland AG 2020

幸福愉悦感

Knowledge Discovery, Knowledge Engineering and Knowledge Management978-3-030-66196-0Series ISSN 1865-0929 Series E-ISSN 1865-0937

金丝雀

规范要多

The Effect of In-Domain Word Embeddings for Chemical Named Entity Recognitionpora. We report the results on three benchmark corpora and conclude that in-domain embeddings statistically significantly improve F1-score on patent corpus but do not lead to any performance gains for chemical articles corpora.

管理员

An Upper Level for What-If Analysisto illustrate the hybrid methodology with a specific case study. This hybridization process will help the user during the design and implementation of a What-If analysis process, overcoming the pitfalls of the conventional What-If analysis process.

兽群

A Hybrid Sliding Window Based Method for Stream Classificationaive Bayes, respectively. In the experiments, accuracy of m-kNN, CSWB-e and CSWB-e2 are analyzed with new data sets in order to observe the relationship between window size and the accuracy. Additionally, the classification performance results for m-kNN are further analyzed and reported in precision

讥笑

A Two-Step Feature Space Transforming Method to Improve Credit Scoring Performanceion: (i) data enhancement; and (ii) data discretization. In the first step, additional meta-features are used in order to improve data discrimination. In the second step, the goal is to reduce the diversity of features. Experiments results performed in real-world datasets with different levels of un

indignant