Browsing Data Science (DS) by Title
Now showing items 64-83 of 158
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Fast algorithm for smoothing parameter selection in multidimensional P-splines
(Statistics and Computing, 2015-12-31)A new computational algorithm for estimating the smoothing parameters of a multidimensional penalized spline generalized model with anisotropic penalty is presented. This new proposal is based on the mixed model representation ... -
Fast estimation of multidimensional adaptive P-spline models
(2016-10-21)A fast and stable algorithm for estimating multidimensional adaptive P-spline models is presented. We call it as Separation of Overlapping Penalties (SOP) as it is an extension of the Separation of Anisotropic Penalties ... -
Fast smoothing parameter separation in multidimensional generalized P-splines: the SAP algorithm
(Statistics and Computing, 2014-12-31)A new computational algorithm for estimating the smoothing parameters of a multidimensional penalized spline generalized linear model with anisotropic penalty is presented. This new proposal is based on the mixed model ... -
Fitting the data from embryo implantation prediction: Learning from label proportions
(Statistical Methods in Medical Research, 2016-01-01)Machine learning techniques have been previously used to assist clinicians to select embryos for human-assisted reproduction. This work aims to show how an appropriate modeling of the problem can contribute to improve ... -
Flexible geostatistical modeling and risk assessment analysis of lead concentration levels of residential soil in the Coeur D'Alene River Basin
(Environmental and Ecological Statistics, 2015-12-31)Soil heavy metals pollution is an urgent problem worldwide. Understanding the spatial distribution of pollutants is critical for environmental management and decision-making. Children and adults are still routinely exposed ... -
A General Framework for Prediction in Generalized Additive Models
(2020-01-13)Smoothing techniques have become one of the most popular modelling approaches in the unidimensional and multidimensional setting. However, out-of-sample prediction in the context of smoothing models is still an open problem ... -
Generalized Nash equilibria for SaaS/PaaS Clouds
(European Journal of Operational Research, 2014-12-31)Cloud computing is an emerging technology that allows to access computing resources on a pay-per-use basis. The main challenges in this area are the efficient performance management and the energy costs minimization. In ... -
Generalized restless bandits and the knapsack problem for perishable inventories
(Operations Research, 2014-12-31)In this paper we introduce the knapsack problem for perishable inventories concerning the optimal dynamic allocation of a collection of products to a limited knapsack. The motivation for designing such a problem comes from ... -
Heavy-traffic analysis of a multiple-phase network with discriminatory processor sharing
(Operations Research, 2011-12-31)We analyze a generalization of the discriminatory processor-sharing (DPS) queue in a heavy-traffic setting. Customers present in the system are served simultaneously at rates controlled by a vector of weights. We assume ... -
Heavy-traffic analysis of the M/PH/1 Discriminatory Processor Sharing queue with phase-dependent weights
(Performance Evaluation Review, 2009-12-31)We analyze a generalization of the Discriminatory Processor Sharing (DPS) queue in a heavy-traffic setting. Customers present in the system are served simultaneously at rates controlled by a vector of weights. We assume ... -
Heavy-traffic revenue maximization in parallel multiclass queues
(Performance Evaluation, 2013-12-31)Motivated by revenue maximization in server farms with admission control, we investigate the optimal scheduling in parallel processor-sharing queues. Incoming customers are distinguished in multiple classes and we define ... -
Hierarchical modelling of patient-reported outcomes data based on the beta-binomial distribution
(2017-12-13)The beta-binomial distribution does not belong to the exponential family and, hence classical regression techniques cannot be used when dealing with outcomes following the mentioned distribution. In this thesis we propose ... -
Hybrid Heuristics for the Linear Ordering Problem
(2019 IEEE Congress on Evolutionary Computation, CEC 2019 - Proceedings, 2019)The linear ordering problem (LOP) is one of the classical NP-Hard combinatorial optimization problems. Motivated by the difficulty of solving it up to optimality, in recent decades a great number of heuristic and meta-heuristic ... -
Hybridizing Cartesian Genetic Programming and Harmony Search for Adaptive Feature Construction in Supervised Learning Problems
(Applied Soft Computing, 2017-02-28)The advent of the so-called Big Data paradigm has motivated a flurry of research aimed at enhancing machine learning models by following very di- verse approaches. In this context this work focuses on the automatic con- ... -
Improved Quantification of Important Beer Quality Parameters based on Non-linear Calibration Methods applied to FT-MIR Spectra
(Analytical and Bioanalytical Chemistry, 2016-07-14)During the production process of beer, it is of utmost importance to guarantee a high consistency of the beer quality. For instance, the bitterness is an essential quality parameter which has to be controlled within the ... -
An investigation of clustering strategies in many-objective optimization: the I-Multi algorithm as a case study
(Swarm Intelligence, 2017-03-30)A variety of general strategies have been applied to enhance the performance of multi-objective optimization algorithms for many-objective optimization problems (those with more than three objectives). One of these strategies ... -
Joint topology optimization, power control and spectrum allocation for intra-vehicular multi-hop sensor networks using dandelion-encoded heuristics
(Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 2016-01-01)In the last years the interest in multi-hop communications has gained momentum within the research community due to the challenging characteristics of the intra-vehicular radio environment and the stringent robustness ... -
K-means for massive data
(2019-04-30)The $K$-means algorithm is undoubtedly one of the most popular clustering analysis techniques, due to its easiness in the implementation, straightforward parallelizability and competitive computational complexity, when ... -
KETpic-Matlab Toolbox for LaTeX High-Quality Graphical Artwork in Educational Materials on Bézier Curve Algorithms at a Master Level
(Lecture Notes in Computer Science, 2017-07)This paper introduces a new toolbox to generate high-quality graphical artwork about the main algorithms for Bézier curves and related topics. The package has been implemented by the authors as a supporting middleware tool ... -
Learning to classify software defects from crowds: a novel approach
(Applied Soft Computing, 2017-11-01)In software engineering, associating each reported defect with a cate- gory allows, among many other things, for the appropriate allocation of resources. Although this classification task can be automated using stan- dard ...