Machine Learning: Envíos recientes
Now showing items 21-40 of 121
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A Multivariate Time Series Streaming Classifier for Predicting Hard Drive Failures [Application Notes]
Ircio, J.; Lojo, A.; Mori, U.; Lozano, J.A.
(2022)
Digital data storage systems such as hard drives can suffer breakdowns that cause the loss of stored data. Due to the cost of data and the damage that its loss entails, hard drive failure prediction is vital. In this ... -
Contributions to Time Series Classification: Meta-Learning and Explainability
Abanda, A. (2021-11-16)
This thesis includes 3 contributions of different types to the area of supervised time series classification, a growing field of research due to the amount of time series collected daily in a wide variety of domains. In ... -
Location Awareness in Beyond 5G Networks
Conti, A.; Morselli, F.; Liu, Z.; Bartoletti, S.; Mazuelas, S.; Lindsey, W.C.; Win, M.Z. (2021-11-01)
Location awareness is essential for enabling contextual services and for improving network management in 5th generation (5G) and beyond 5G (B5G) networks. This paper provides an overview of the expanding opportunities ... -
Alternative Representations for Codifying Solutions in Permutation-Based Problems
(2020-07-01)Since their introduction, Estimation of Distribution Algorithms (EDAs) have proved to be very competitive algorithms to solve many optimization problems. However, despite recent developments, in the case of permutation-based ... -
On solving cycle problems with Branch-and-Cut: extending shrinking and exact subcycle elimination separation algorithms
Kobeaga, G.; Merino, M.; Lozano, J.A. (2021-01-01)
In this paper, we extend techniques developed in the context of the Travelling Salesperson Problem for cycle problems. Particularly, we study the shrinking of support graphs and the exact algorithms for subcycle elimination ... -
Statistical assessment of experimental results: a graphical approach for comparing algorithms
Arza, E.; Ceberio, J.; Irurozki, E.; Pérez, A. (2021-08-25)
Non-deterministic measurements are common in real-world scenarios: the performance of a stochastic optimization algorithm or the total reward of a reinforcement learning agent in a chaotic environment are just two examples ... -
A Review on Outlier/Anomaly Detection in Time Series Data
Blázquez-García, A.; Conde, A.; Mori, U.; Lozano, J.A. (2021)
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 ... -
Water leak detection using self-supervised time series classification
Blázquez-García, A.; Conde, A.; Mori, U.; Lozano, J.A. (2021)
Leaks in water distribution networks cause a loss of water that needs to be com- pensated to ensure a continuous supply for all customers. This compensation is achieved by increasing the flow of the network, which entails ... -
A cheap feature selection approach for the K -means algorithm
Capo, M.; Pérez, A.; Lozano, J.A. (2021-05)
The increase in the number of features that need to be analyzed in a wide variety of areas, such as genome sequencing, computer vision or sensor networks, represents a challenge for the K-means algorithm. In this regard, ... -
Statistical model for reproducibility in ranking-based feature selection
Urkullu, A.; Pérez, A.; Calvo, B. (2020-11-05)
The stability of feature subset selection algorithms has become crucial in real-world problems due to the need for consistent experimental results across different replicates. Specifically, in this paper, we analyze the ... -
On the symmetry of the Quadratic Assignment Problem through Elementary Landscape Decomposition
(2021-07)When designing meta-heuristic strategies to optimize the quadratic assignment problem (QAP), it is important to take into account the specific characteristics of the instance to be solved. One of the characteristics that ... -
Exploring Gaps in DeepFool inSearch of More Effective Adversarial Perturbations
Vadillo, J.; Santana, R.; Lozano, J.A. (2021)
Adversarial examples are inputs subtly perturbed to produce a wrong prediction in machine learning models, while remaining perceptually similar to the original input. To find adversarial examples, some attack strategies ... -
Delineation of site‐specific management zones using estimation of distribution algorithms
Lozano, J.A.; Velasco, J.; Vicencio, S.; Cid-García, N.M. (2021)
In this paper, we present a novel methodology to solve the problem of delineating homogeneous site-specific management zones (SSMZ) in agricultural fields. This problem consists of dividing the field into small regions for ... -
On the fair comparison of optimization algorithms in different machines
Arza, E.; Pérez, A.; Ceberio, J.; Irurozki, E. (2021)
An experimental comparison of two or more optimization algorithms requires the same computational resources to be assigned to each algorithm. When a maximum runtime is set as the stopping criterion, all algorithms need to ... -
Efficient meta-heuristics for spacecraft trajectory optimization
Shirazi, A. (2021-03)
Meta-heuristics has a long tradition in computer science. During the past few years, different types of meta-heuristics, specially evolutionary algorithms got noticeable attention in dealing with real-world optimization ... -
Algorithms for Large Orienteering Problems
Kobeaga, G. (2021-01)
In this thesis, we have developed algorithms to solve large-scale Orienteering Problems. The Orienteering Problem is a combinatorial optimization problem were given a weighted complete graph with vertex profits and a maximum ... -
Simulation Framework for Orbit Propagation and Space Trajectory Visualization
Shirazi, A.; Ceberio, J.; Lozano, J.A. (2021)
In this paper, an interactive tool for simulation of satellites dynamics and autonomous spacecraft guidance is presented. Different geopotential models for orbit propagation of Earth-orbiting satellites are provided, which ... -
Minimax Classification with 0-1 Loss and Performance Guarantees
Mazuelas, S.; Zanoni, A.; Pérez, A. (2020-12-01)
Supervised classification techniques use training samples to find classification rules with small expected 0-1 loss. Conventional methods achieve efficient learning and out-of-sample generalization by minimizing surrogate ... -
Analysis of the sensitivity of the End-Of-Turn Detection task to errors generated by the Automatic Speech Recognition process.
Montenegro, C.; Santana, R.; Lozano, J.A. (2021)
An End-Of-Turn Detection Module (EOTD-M) is an essential component of au- tomatic Spoken Dialogue Systems. The capability of correctly detecting whether a user’s utterance has ended or not improves the accuracy in interpreting ... -
Probabilistic Load Forecasting Based on Adaptive Online Learning
Álvarez, V.; Mazuelas, S.; Lozano, J.A. (2020)
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 ...