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Data miners' little helper: data transformation activity cues for cluster analysis on document collections.

, , , и . WIMS, стр. 27:1-27:6. ACM, (2017)

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Francesco Bettella

Protein secondary structure prediction using optimized scoring functions: a comparative statistical method. FU Berlin, (2009)
 

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Data miners' little helper: data transformation activity cues for cluster analysis on document collections., , , и . WIMS, стр. 27:1-27:6. ACM, (2017)Automating concept-drift detection by self-evaluating predictive model degradation., , , , и . CoRR, (2019)What's in the box? Explaining the black-box model through an evaluation of its interpretable features., и . CoRR, (2019)Prompting the data transformation activities for cluster analysis on collections of documents., , , и . SEBD, том 2037 из CEUR Workshop Proceedings, стр. 226. CEUR-WS.org, (2017)Self-tuning techniques for large scale cluster analysis on textual data collections., , и . SAC, стр. 771-776. ACM, (2017)Trusting deep learning natural-language models via local and global explanations., , , и . Knowl. Inf. Syst., 64 (7): 1863-1907 (2022)Towards a real-time unsupervised estimation of predictive model degradation., , , , и . BIRTE, стр. 5:1-5:6. ACM, (2019)Expand your Training Limits! Generating Training Data for ML-based Data Management., , , и . SIGMOD Conference, стр. 1865-1878. ACM, (2021)iSTEP, an Integrated Self-Tuning Engine for Predictive Maintenance in Industry 4.0., , , , , , и . ISPA/IUCC/BDCloud/SocialCom/SustainCom, стр. 924-931. IEEE, (2018)Black-Box Model Explained Through an Assessment of Its Interpretable Features., , и . ADBIS (Short Papers and Workshops), том 909 из Communications in Computer and Information Science, стр. 138-149. Springer, (2018)