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of 21
pro vyhledávání: '"Júnior Sylvio Barbon"'
The Problem-oriented AutoML in Clustering (PoAC) framework introduces a novel, flexible approach to automating clustering tasks by addressing the shortcomings of traditional AutoML solutions. Conventional methods often rely on predefined internal Clu
Externí odkaz:
http://arxiv.org/abs/2409.16218
Autor:
Junior, Sylvio Barbon, Ceravolo, Paolo, Groppe, Sven, Jarrar, Mustafa, Maghool, Samira, Sèdes, Florence, Sahri, Soror, Van Keulen, Maurice
A Language Model is a term that encompasses various types of models designed to understand and generate human communication. Large Language Models (LLMs) have gained significant attention due to their ability to process text with human-like fluency a
Externí odkaz:
http://arxiv.org/abs/2406.06596
Understanding the decisions of tree-based ensembles and their relationships is pivotal for machine learning model interpretation. Recent attempts to mitigate the human-in-the-loop interpretation challenge have explored the extraction of the decision
Externí odkaz:
http://arxiv.org/abs/2404.02942
Machine learning models are routinely integrated into process mining pipelines to carry out tasks like data transformation, noise reduction, anomaly detection, classification, and prediction. Often, the design of such models is based on some ad-hoc a
Externí odkaz:
http://arxiv.org/abs/2306.10341
Process simulation is gaining attention for its ability to assess potential performance improvements and risks associated with business process changes. The existing literature presents various techniques, generally grounded in process models discove
Externí odkaz:
http://arxiv.org/abs/2303.17879
Autor:
Mantovani, Rafael Gomes, Rossi, André Luis Debiaso, Alcobaça, Edesio, Gertrudes, Jadson Castro, Junior, Sylvio Barbon, de Carvalho, André Carlos Ponce de Leon Ferreira
Machine Learning (ML) algorithms have been increasingly applied to problems from several different areas. Despite their growing popularity, their predictive performance is usually affected by the values assigned to their hyperparameters (HPs). As con
Externí odkaz:
http://arxiv.org/abs/2008.00025
Akademický článek
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Autor:
Mantovani, Rafael Gomes, Horváth, Tomáš, Rossi, André L. D., Cerri, Ricardo, Junior, Sylvio Barbon, Vanschoren, Joaquin, de Carvalho, André Carlos Ponce de Leon Ferreira
Machine learning algorithms often contain many hyperparameters (HPs) whose values affect the predictive performance of the induced models in intricate ways. Due to the high number of possibilities for these HP configurations and their complex interac
Externí odkaz:
http://arxiv.org/abs/1812.02207
Autor:
da Costa, Victor Guilherme Turrisi, de Carvalho, André Carlos Ponce de Leon Ferreira, Junior, Sylvio Barbon
Dealing with memory and time constraints are current challenges when learning from data streams with a massive amount of data. Many algorithms have been proposed to handle these difficulties, among them, the Very Fast Decision Tree (VFDT) algorithm.
Externí odkaz:
http://arxiv.org/abs/1805.06368
Autor:
Fonseca, Everthon Silva, Guido, Rodrigo Capobianco ⁎, Junior, Sylvio Barbon, Dezani, Henrique, Gati, Rodrigo Rosseto, Mosconi Pereira, Denis César
Publikováno v:
In Biomedical Signal Processing and Control January 2020 55
