Exploració per autor "Belanche Muñoz, Luis Antonio"
Ara es mostren els items 55-74 de 84
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Learning in networks of similarity processing neurons
(2013)
Text en actes de congrés
Accés obertSimilarity functions are a very flexible container under which to express knowledge about a problem as well as to capture the meaningful relations in input space. In this paper we describe ongoing research using similarity ... -
Learning with heterogeneous neural networks
(Nova Science Publishers, Inc. New York, 2011)
Capítol de llibre
Accés restringit per política de l'editorialThis chapter studies a class of neuron models that computes a user-defined similarity function between inputs and weights. The neuron transfer function is formed by composition of an adapted logistic function with the ... -
Machine learning methods for classifying normal vs. tumorous tissue with spectral data
(2009)
Text en actes de congrés
Accés obertMachine learning is a powerful paradigm within which to analyze 1H-MRS spectral data for the automated classi¯cation of tumor pathologies aimed to facilitate clinical diagnosis. The high dimensionality of the involved ... -
Els mètodes kernel i perquè els hauríem d'estimar
(2017-03-08)
Audiovisual
Accés obertMolts problemes d'aprenentatge automàtic es poden formular com problemes clàssics de l'estadística multivariant; per exemple, el reconeixement de patrons, la regressió o la reducció de la dimensió. Cada cop és més freqüent ... -
Modeling the input-output behaviour of wastewater treatment plants using soft computing techniques
(1998)
Text en actes de congrés
Accés obertWastewater Treatment Plants (WWTPs) control and prediction under a wide range of operating conditions is an important goal in order to avoid breaking of environmental balance, keep the system in stable operating conditions ... -
Multivariate dynamic kernels for financial time series forecasting
(Springer, 2016)
Text en actes de congrés
Accés obertWe propose a forecasting procedure based on multivariate dynamic kernels, with the capability of integrating information measured at different frequencies and at irregular time intervals in financial markets. A data ... -
New multiplatform computer program for numerical identification of microorganisms
(2009-12)
Article
Accés restringit per política de l'editorialThe classification of bacteria by using genomic methods or expensive biochemical-based commercial kits is sometimes beyond the reach of many laboratories that need to perform numerous classifications of unknown bacterial ... -
Off-the-grid: Fast and effective hyperparameter search for kernel clustering
(Springer, 2020)
Text en actes de congrés
Accés obertKernel functions are a powerful tool to enhance the k-means clustering algorithm via the kernel trick. It is known that the parameters of the chosen kernel function can have a dramatic impact on the result. In supervised ... -
On aggregation operators of transitive similarity and dissimilarity relations
(Institute of Electrical and Electronics Engineers (IEEE), 2004)
Text en actes de congrés
Accés obertSimilarity and dissimilarity are widely used concepts. One of the most studied matters is their combination or aggregation. However, transitivity property is often ignored when aggregating despite being a highly important ... -
On some strategies for missing values in positive semidefinite matrices
(Thompson, 2005)
Text en actes de congrés
Accés obertThis article presents our work on missing values in Positive Semi-Definite or PSD matrices. We show how simple properties of PSD matrices can be used to deal with missing values. We study several situations and investigate ... -
On the selection of hidden neurons with heuristic search strategies for approximation
(2006)
Text en actes de congrés
Accés obertFeature Selection techniques usually follow some search strategy to select a suitable subset from a set of features. Most neural network growing algorithms perform a search with Forward Selection with the objective of ... -
Parsimonious selection of useful genes in microarray gene expression data
(Springer, 2011)
Capítol de llibre
Accés obertMachine Learning methods have of late made significant efforts to solving multidisciplinary problems in the field of cancer classification in microarray gene expression data. These tasks are characterized by a large number ... -
Predicting fecal sources in waters with diverse pollution loads using general and molecular host-specific indicators and applying machine learning methods
(2015-03-15)
Article
Accés obertIn this study we use a machine learning software (Ichnaea) to generate predictive models for water samples with different concentrations of fecal contamination (point source, moderate and low). We applied several MST methods ... -
Predicting software anomalies using machine learning techniques
(IEEE Computer Society Publications, 2011)
Text en actes de congrés
Accés obertIn this paper, we present a detailed evaluation of a set of well-known Machine Learning classifiers in front of dynamic and non-deterministic software anomalies. The system state prediction is based on monitoring system ... -
Remainder subset awareness for feature subset selection
(Thomson Editores Spain, 2007)
Text en actes de congrés
Accés obertFeature subset selection has become more and more a common topic of research. This popularity is partly due to the growth in the number of features and application domains. The family of algorithms known as plus-l-minus-r ... -
Remainder subset awareness for feature subset selection
(Springer-Verlag, 2009)
Text en actes de congrés
Accés obertFeature subset selection has become more and more a common topic of research. This popularity is partly due to the growth in the number of features and application domains. It is of the greatest importance to take themost ... -
Search strategies guided by the evidence for the selection of basis functions in regression
(Institute of Electrical and Electronics Engineers (IEEE), 2007)
Text en actes de congrés
Accés obertThis work addresses the problem of selecting a subset of basis functions for a model linear in the parameters for regression tasks. Basis functions from a set of candidates are explicitly selected with search methods coming ... -
Selection of basis functions guided by the L2 soft margin
(Springer, 2007)
Text en actes de congrés
Accés obertSupport Vector Machines (SVMs) for classification tasks produce sparse models by maximizing the margin. Two limitations of this technique are considered in this work: firstly, the number of support vectors can be large ... -
Similarity networks for classification: a case study in the Horse Colic problem
(2014)
Report de recerca
Accés obertThis paper develops a two-layer neural network in which the neuron model computes a user-defined similarity function between inputs and weights. The neuron transfer function is formed by composition of an adapted logistic ... -
Similarity networks for heterogeneous data
(I6doc.com, 2012)
Text en actes de congrés
Accés obertA two-layer neural network is developed in which the neuron model computes a user-defined similarity function between inputs and weights. The neuron model is formed by the composition of an adapted logistic function with ...
