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

Magnanimous

書目名稱Knowledge Discovery, Knowledge Engineering and Knowledge Management影響因子(影響力)




書目名稱Knowledge Discovery, Knowledge Engineering and Knowledge Management影響因子(影響力)學(xué)科排名




書目名稱Knowledge Discovery, Knowledge Engineering and Knowledge Management網(wǎng)絡(luò)公開度




書目名稱Knowledge Discovery, Knowledge Engineering and Knowledge Management網(wǎng)絡(luò)公開度學(xué)科排名




書目名稱Knowledge Discovery, Knowledge Engineering and Knowledge Management被引頻次




書目名稱Knowledge Discovery, Knowledge Engineering and Knowledge Management被引頻次學(xué)科排名




書目名稱Knowledge Discovery, Knowledge Engineering and Knowledge Management年度引用




書目名稱Knowledge Discovery, Knowledge Engineering and Knowledge Management年度引用學(xué)科排名




書目名稱Knowledge Discovery, Knowledge Engineering and Knowledge Management讀者反饋




書目名稱Knowledge Discovery, Knowledge Engineering and Knowledge Management讀者反饋學(xué)科排名

transplantation

強(qiáng)制性

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

幸福愉悅感

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

金絲雀

規(guī)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.

管理員

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.

獸群

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

譏笑

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