Jesús Prada Alonso

Jesús Prada Alonso
Autonomous University of Madrid | UAM · GAA

PhD in Machine Learning

About

9
Publications
1,743
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70
Citations
Introduction
Jesús Prada Alonso currently works at the GAA, Universidad Autónoma de Madrid. Jesús does research in Artificial Neural Network and Artificial Intelligence. Their most recent publication is 'Deep Support Vector Classification and Regression'.

Publications

Publications (9)
Article
Full-text available
Determining the eruption frequency-Magnitude (f-M) relationship for data-limited volcanoes is challenging since it requires a comprehensive eruption record of the past eruptive activity. This is the case for Melimoyu, a long-dormant and data-limited volcano in the Southern Volcanic Zone (SVZ) in Chile with only two confirmed Holocene eruptions (VEI...
Article
Full-text available
The pandemic caused by coronavirus COVID-19 has already had a massive impact in our societies in terms of health, economy, and social distress. One of the most common symptoms caused by COVID-19 are lung problems like pneumonia, which can be detected using X-ray images. On the other hand, the popularity of Machine Learning models has grown expone...
Article
Kernel based Support Vector Machines, SVM, one of the most popular machine learning models, usually achieve top performances in two-class classification and regression problems. However, their training cost is at least quadratic on sample size, making them thus unsuitable for large sample problems. However, Deep Neural Networks (DNNs), with a cost...
Conference Paper
Support Vector Machines, SVM, are one of the most popular machine learning models for supervised problems and have proved to achieve great performance in a wide broad of predicting tasks. However, they can suffer from scalability issues when working with large sample sizes, a common situation in the big data era. On the other hand, Deep Neural Netwo...
Article
Full-text available
General noise cost functions have been recently proposed for support vector regression (SVR). When applied to tasks whose underlying noise distribution is similar to the one assumed for the cost function, these models should perform better than classical \(\epsilon\)-SVR. On the other hand, uncertainty estimates for SVR have received a somewhat lim...
Conference Paper
Full-text available
Building uncertainty estimates is still an open problem for most machine learning regression models. On the other hand, general noise–dependent cost functions have been recently proposed for Support Vector Regression, SVR, which should be more effective when applied to regression problems whose underlying noise distribution follows the one assumed...
Conference Paper
Full-text available
Twitter is an extremely popular microblogging website where users read and write millions of short messages, each one containing a maximum of 140 characters. With more than 500 million users as of December 2014, this social network generates millions of messages (known as tweets) on a wide variety of topics every day, ranging from personal informat...
Thesis
Full-text available
While Support Vector Regression, SVR, is one of the algorithms of choice in modeling problems, construction of its error intervals seems to have received less attention. In addition, general noise cost functions for SVR have been recently proposed and proved to be more effective when a noise distribution that fits the data is properly chosen. Taki...
Conference Paper
Full-text available
While Support Vector Regression, SVR, is one of the algorithms of choice in modeling problems, construction of its error intervals seems to have received less attention. On the other hand, general noise cost functions for SVR have been recently proposed. Taking this into account, this paper describes a direct approach to build error intervals for d...

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