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Jose Luis Vieira Sobrinho, Flavio Henrique Teles Vieira and Alisson Assis Cardoso
The high dimensionality of real-life datasets is one of the biggest challenges in the machine learning field. Due to the increased need for computational resources, the higher the dimension of the input data is, the more difficult the learning task will ...
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Man-Ni Chu and Yu-Chun Wang
The goal of this study was to compare model-generated sounds with the process of sound acquisition in humans. The research utilized two dictionaries of the Chaoshan dialect spanning approximately one century. Identical Chinese characters were selected fr...
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Rogério Duarte, Ângela Lacerda Nobre, Fernando Pimentel and Marc Jacquinet
Accreditation bodies call for curriculum development processes that are open to all stakeholders, reflecting viewpoints of students, industry, university faculty, and society. However, communication difficulties between faculty and non-faculty groups lea...
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Luis M. de Campos, Juan M. Fernández-Luna, Juan F. Huete, Francisco J. Ribadas-Pena and Néstor Bolaños
In the context of academic expert finding, this paper investigates and compares the performance of information retrieval (IR) and machine learning (ML) methods, including deep learning, to approach the problem of identifying academic figures who are expe...
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Marco Scutari
Bayesian networks (BNs) are a foundational model in machine learning and causal inference. Their graphical structure can handle high-dimensional problems, divide them into a sparse collection of smaller ones, underlies Judea Pearl?s causality, and determ...
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