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Interpretable sparse SIR for functional data., , и . Stat. Comput., 29 (2): 255-267 (2019)Which Dissimilarity Is to Be Used When Extracting Typologies in Sequence Analysis? A Comparative Study., , и . IWANN (1), том 7902 из Lecture Notes in Computer Science, стр. 69-79. Springer, (2013)Random Forests for Big Data., , , и . CoRR, (2015)On Combining Wavelets Expansion and Sparse Linear Models for Regression on Metabolomic Data and Biomarker Selection., , , , , и . Commun. Stat. Simul. Comput., 45 (1): 282-298 (2016)System Model Network for Adipose Tissue Signatures Related to Weight Changes in Response to Calorie Restriction and Subsequent Weight Maintenance., , , , , , , , , и . PLoS Comput. Biol., (2015)Neural Networks for Complex Data., , , , и . Künstliche Intell., 26 (4): 373-380 (2012)Theoretical and Applied Aspects of the Self-Organizing Maps., , , и . WSOM, том 428 из Advances in Intelligent Systems and Computing, стр. 3-26. Springer, (2016)Random Forests for Big Data., , , и . Big Data Res., (2017)Multiple Kernel Self-Organizing Maps., , и . ESANN, (2013)A comparison of eight metamodeling techniques for the simulation of N2O fluxes and N leaching from corn crops., , , и . Environ. Model. Softw., (2012)