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Bibliografía básica
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Cunningham, P. (2009). Dimension reduction. In Machine learning techniques for multimedia: Case studies on organization and retrieval . Springer Berlin Heidelberg
Breiman, L. (2001). Random Forest. Machine learning, 45, 5-32.
Hastie, T., Rosset, S., Zhu, J., & Zou, H. (2009). Multi-class adaboost. Statistics and its Interface, 2(3), 349-360
Ke, G., Meng, Q., Finley, T., Wang, T., Chen, W., Ma, W., ... & Liu, T. Y. (2017). Lightgbm: A highly efficient gradient boosting decision tree.. Advances in neural information processing systems, 30
Chen, T., He, T., Benesty, M., Khotilovich, V., Tang, Y., Cho, H.,& Zhou, T. (2015). Xgboost: extreme gradient boosting.. arxiv
Prokhorenkova, L., Gusev, G., Vorobev, A., Dorogush, A. V., & Gulin, A (2018). CatBoost: unbiased boosting with categorical features. Advances in neural information processing systems, 31.
Dong, X., Yu, Z., Cao, W., Shi, Y., & Ma, Q. (2020). A survey on ensemble learning. Frontiers of Computer Science, 14, 241-258
Caruana, R. (1997). Multitask learning.. Machine learning, 28, 41-75
Nizar Bouguila, Wentao Fan, Manar Amayri (Eds.) (2022). Hidden Markov Models and Applications. Springer
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Bibliografía complementaria
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Pearson, K. (1901). Sobre líneas y planos de ajuste más cercano a sistemas de puntos en el espacio. Philosophical Magazine 2 (11): 559-572
Meng, C., Zeleznik, O. A., Thallinger, G. G., Kuster, B., Gholami, A. M., & Culhane, A. C. (2016). Dimension reduction techniques for the integrative analysis of multi-omics data. Briefings in bioinformatics, 17(4), 628-641
Abelson, R. P., & Prentice, D. A. (1997). Contrast tests of interaction hypothesis. Psychological Methods, 2(4), 315
Zhang, Y., & Yang, Q. (2021). A survey on multi-task learning.. IEEE transactions on knowledge and data engineering, 34(12), 5586-5609.
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