Browsing Machine Learning by Title
Now showing items 120 of 113

An adaptive neuroevolutionbased hyperheuristic
(2020)According to the NoFreeLunch theorem, an algorithm that performs efficiently on any type of problem does not exist. In this sense, algorithms that exploit problemspecific knowledge usually outperform more generic ... 
Advances on Time Series Analysis using Elastic Measures of Similarity
(20200723)A sequence is a collection of data instances arranged in a structured manner. When this arrangement is held in the time domain, sequences are instead referred to as time series. As such, each observation in a time series ... 
Aggregated outputs by linear models: An application on marine litter beaching prediction
(20190101)In regression, a predictive model which is able to anticipate the output of a new case is learnt from a set of previous examples. The output or response value of these examples used for model training is known. When learning ... 
Algorithms for Large Orienteering Problems
(202101)In this thesis, we have developed algorithms to solve largescale Orienteering Problems. The Orienteering Problem is a combinatorial optimization problem were given a weighted complete graph with vertex profits and a maximum ... 
Alternative Representations for Codifying Solutions in PermutationBased Problems
(20200701)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 permutationbased ... 
An empirical study on collective intelligence algorithms for video games problemsolving
(20151231)Computational intelligence (CI), such as evolutionary computation or swarm intelligence methods, is a set of bioinspired algorithms that have been widely used to solve problems in areas like planning, scheduling or ... 
Analysis of a hybrid Genetic Simulated Annealing strategy applied in multiobjective optimization of orbital maneuvers
(20170327)Optimization of orbital maneuvers is one of the main issues in conceptual and preliminary design of spacecraft in different space missions. The main issue in optimization of highthrust orbit transfers is that the common ... 
Analysis of Dominant Classes in Universal Adversarial Perturbations
(2022)The reasons why Deep Neural Networks are susceptible to being fooled by adversarial examples remains an open discussion. Indeed, many differ ent strategies can be employed to efficiently generate adversarial attacks, some ... 
Analysis of the sensitivity of the EndOfTurn Detection task to errors generated by the Automatic Speech Recognition process.
(2021)An EndOfTurn Detection Module (EOTDM) 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 ... 
Analyzing rare event, anomaly, novelty and outlier detection terms under the supervised classification framework
(20190901)In recent years, a variety of research areas have contributed to a set of related problems with rare event, anomaly, novelty and outlier detection terms as the main actors. These multiple research areas have created a ... 
Anatomy of the attraction basins: Breaking with the intuition
(2019)olving combinatorial optimization problems efficiently requires the development of algorithms that consider the specific properties of the problems. In this sense, local search algorithms are designed over a neighborhood ... 
Application of machine learning techniques to weather forecasting
(20181024)Weather forecasting is, still today, a human based activity. Although computer simulations play a major role in modelling the state and evolution of the atmosphere, there is a lack of methodologies to automate the ... 
Approaching the Quadratic Assignment Problem with Kernels of Mallows Models under the Hamming Distance
(201907)The Quadratic Assignment Problem (QAP) is a specially challenging permutationbased nphard combinatorial optimization problem, since instances of size $n>40$ are seldom solved using exact methods. In this sense, many ... 
Are the artificially generated instances uniform in terms of difficulty?
(201806)In the field of evolutionary computation, it is usual to generate artificial benchmarks of instances that are used as a testbed to determine the performance of the algorithms at hand. In this context, a recent work on ... 
Bayesian inference for algorithm ranking analysis
(20180830)The statistical assessment of the empirical comparison of algorithms is an essential step in heuristic optimization. Classically, researchers have relied on the use of statistical tests. However, recently, concerns about ... 
Bayesian Optimization Approaches for Massively Multimodal Problems
(2019)The optimization of massively multimodal functions is a challenging task, particularly for problems where the search space can lead the op timization process to local optima. While evolutionary algorithms have been ... 
Belief Condensation Filtering For RssiBased State Estimation In Indoor Localization
(2019)Recent advancements in signal processing and communication systems have resulted in evolution of an intriguing concept referred to as Internet of Things (IoT). By embracing the IoT evolution, there has been a surge of ... 
Calibration Model Maintenance in Melamine Resin Production: Integrating Drift Detection, Smart Sample Selection and Model Adaptation
(2018)The physicochemical properties of Melamine Formaldehyde (MF) based thermosets are largely influenced by the degree of polymerization (DP) in the underlying resin. Online supervision of the turbidity point by means of ... 
Characterising the rankings produced by combinatorial optimisation problems and finding their intersections
(2019)The aim of this paper is to introduce the concept of intersection between combinatorial optimisation problems. We take into account that most algorithms, in their machinery, do not consider the exact objective function ... 
A cheap feature selection approach for the K means algorithm
(202105)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 Kmeans algorithm. In this regard, ...