Browsing by Author "Belanche Muñoz, Luis Antonio"
Now showing items 21-40 of 86
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Developing professional skills at tertiary level: A model to integrate competencies across the curriculum
(Institute of Electrical and Electronics Engineers (IEEE), 2014)
Conference report
Open AccessIn the context of the European Higher Education Area, curriculum design needs to be based on the defined competencies of each degree programs, including both domain specific and professional competencies. In this educational ... -
Developments in kernel design
(2013)
Conference report
Open AccessThe aim of this paper is to give a concise overview of kernels, with a special attention to non-standard or heterogeneous data sources (e.g. non-numerical or structured data). A second goal is to discuss the world of ... -
Differentiation of glioblastomas and metastases using 1H-MRS spectral data
(CSREA Press, 2010)
Conference report
Open AccessHydrogen-1 magnetic resonance spectroscopy (1H-MRS) allows noninvasive in vivo quantification of metabolite concentrations in brain tissue. In this work two of the most aggressive brain tumors are studied with the purpose ... -
Distance-based kernels for real-valued data
(Springer, 2008)
Conference report
Open AccessWe consider distance-based similarity measures for real-valued vectors of interest in kernel-based machine learning algorithms. In particular, a truncated Euclidean similarity measure and a self-normalized similarity measure ... -
Effective classification and gene expression profiling for the facioscapulohumeral muscular dystrophy
(2013-12-13)
Article
Open AccessThe Facioscapulohumeral Muscular Dystrophy (FSHD) is an autosomal dominant neuromuscular disorder whose incidence is estimated in about one in 400,000 to one in 20,000. No effective therapeutic strategies are known to halt ... -
Evolutionary optimization of heterogeneous problems
(2003-02)
Research report
Open AccessA large number of practical optimization problems involve elements of quite diverse nature, described as mixtures of qualitative and quantitative information, and whose description is possibly incomplete. In this work we ... -
Evolutionary optimization of heterogeneous problems
(Springer, 2002)
Conference report
Open AccessA large number of practical optimization problems involve elements of quite diverse nature described as mixtures of qualitative and quantitative information and whose description is possibly incomplete. In this work we ... -
Exploiting the accumulated evidence for gene selection in microarray gene expression data
(2013)
Research report
Open AccessMachine Learning methods have of late made signicant efforts to solving multidisciplinary problems in the field of cancer classification using microarray gene expression data. Feature subset selection methods can play an ... -
Exploiting the accumulated evidence for gene selection in microarray gene expression data
(IOS Press, 2010)
Conference report
Open AccessFeature subset selection (FSS) methods play an important role for cancer classification using microarray gene expression data. In this scenario, it is extremely important to select genes by taking into account the possible ... -
Extended linear models with Gaussian prior on the parameters and adaptive expansion vectors
(Springer, 2007)
Conference report
Open AccessWe present an approximate Bayesian method for regression and classification with models linear in the parameters. Similar to the Relevance Vector Machine (RVM), each parameter is associated with an expansion vector. Unlike ... -
Feature and model selection in 1H-MRS single voxel spectra for cancer classification
(Future Technology Press, 2009)
Part of book or chapter of book
Restricted access - publisher's policyMachine learning is a powerful paradigm within which to analyze 1HMRS spectral data for the classification of tumour pathologies. An important characteristic of this task is the high dimensionality of the involved data ... -
Feature selection algorithms: a survey and experimental evaluation
(Institute of Electrical and Electronics Engineers (IEEE), 2002)
Conference report
Open AccessIn view of the substantial number of existing feature selection algorithms, the need arises to count on criteria that enables to adequately decide which algorithm to use in certain situations. This work assesses the ... -
Feature selection algorithms: a survey and experimental evaluation
(2003-02)
Research report
Open AccessIn view of the substantial number of existing feature selection algorithms, the need arises to count on criteria that enables to adequately decide which algorithm to use in certain situations. This work reviews several ... -
Feature selection for microarray gene expression data using simulated annealing guided by the multivariate joint entropy
(2013)
Research report
Open AccessIn this work a new way to calculate the multivariate joint entropy is presented. This measure is the basis for a fast information-theoretic based evaluation of gene relevance in a Microarray Gene Expression data context. ... -
Feature selection for microarray gene expression data using simulated annealing guided by the multivariate joint entropy
(2014-04-01)
Article
Open AccessMicroarray classification poses many challenges for data analysis, given that a gene expression data set may consist of dozens of observations with thousands or even tens of thousands of genes. In this context, feature ... -
Feature selection for the prediction and visualization of brain tumor types using proton magnetic resonance spectroscopy data
(Springer, 2012)
Part of book or chapter of book
Restricted access - publisher's policyIn cancer diagnosis, classification of the different tumor types is of great importance. An accurate prediction of basic tumor types provides better treatment and may minimize the negative impact of incorrectly targeted ... -
Feature selection in proton magnetic resonance spectroscopy data of brain tumors
(Università degli Studi di Salerno, 2011)
Conference report
Open AccessIn cancer diagnosis, classification of the different tumor types is of great importance. An accurate prediction of different tumor types provides better treatment and may minimize the negative impact of incorrectly targeted ... -
Feature selection in proton magnetic resonance spectroscopy for brain tumor classification
(2008)
Conference report
Open AccessH-MRS is a technique that uses response of protons under certain magnetic conditions to reveal the biochemical structure of human tissue. An important application is found in brain tumor diagnosis, due to the known ... -
Forecasting financial time series with multiple kernel learning
(2017)
Conference lecture
Restricted access - publisher's policyThis paper introduces a forecasting procedure based on mul-tivariate dynamic kernels to re-examine –under a non linear framework–the experimental tests reported by Welch and Goyal showing that severalvariables proposed in ... -
Fuzzy heterogeneous heurons for imprecise classification problems
(1998-06)
Research report
Open AccessIn the classical neuron model, inputs are continuous real-valued quantities. However, in many important domains from the real world, objects are described by a mixture of continuous and discrete variables, usually containing ...
