Zobrazeno 1 - 10
of 16
pro vyhledávání: '"Lozano, Jose A."'
Autor:
Zaballa, Onintze, Pérez, Aritz, Gómez-Inhiesto, Elisa, Acaiturri-Ayesta, Teresa, Lozano, Jose A.
Electronic health records contain valuable information for monitoring patients' health trajectories over time. Disease progression models have been developed to understand the underlying patterns and dynamics of diseases using these data as sequences
Externí odkaz:
http://arxiv.org/abs/2311.09369
For a sequence of classification tasks that arrive over time, it is common that tasks are evolving in the sense that consecutive tasks often have a higher similarity. The incremental learning of a growing sequence of tasks holds promise to enable acc
Externí odkaz:
http://arxiv.org/abs/2310.15974
The statistical characteristics of instance-label pairs often change with time in practical scenarios of supervised classification. Conventional learning techniques adapt to such concept drift accounting for a scalar rate of change by means of a care
Externí odkaz:
http://arxiv.org/abs/2205.15942
Load forecasting is crucial for multiple energy management tasks such as scheduling generation capacity, planning supply and demand, and minimizing energy trade costs. Such relevance has increased even more in recent years due to the integration of r
Externí odkaz:
http://arxiv.org/abs/2011.14721
Publikováno v:
Journal of Machine Learning Research, 24(15):1-42, 2023
Despite the remarkable performance and generalization levels of deep learning models in a wide range of artificial intelligence tasks, it has been demonstrated that these models can be easily fooled by the addition of imperceptible yet malicious pert
Externí odkaz:
http://arxiv.org/abs/2004.06383
Recent advances in technology have brought major breakthroughs in data collection, enabling a large amount of data to be gathered over time and thus generating time series. Mining this data has become an important task for researchers and practitione
Externí odkaz:
http://arxiv.org/abs/2002.04236
Choosing the most adequate kernel is crucial in many Machine Learning applications. Gaussian Process is a state-of-the-art technique for regression and classification that heavily relies on a kernel function. However, in the Gaussian Process literatu
Externí odkaz:
http://arxiv.org/abs/1910.05173
Sentiment analysis consists of evaluating opinions or statements from the analysis of text. Among the methods used to estimate the degree in which a text expresses a given sentiment, are those based on Gaussian Processes. However, traditional Gaussia
Externí odkaz:
http://arxiv.org/abs/1904.00977
Time series classification is an increasing research topic due to the vast amount of time series data that are being created over a wide variety of fields. The particularity of the data makes it a challenging task and different approaches have been t
Externí odkaz:
http://arxiv.org/abs/1806.04509
The analysis of continously larger datasets is a task of major importance in a wide variety of scientific fields. In this sense, cluster analysis algorithms are a key element of exploratory data analysis, due to their easiness in the implementation a
Externí odkaz:
http://arxiv.org/abs/1801.02949