Exploració per autor "Belanche Muñoz, Luis Antonio"
Ara es mostren els items 31-50 de 84
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Feature and model selection in 1H-MRS single voxel spectra for cancer classification
(Future Technology Press, 2009)
Capítol de llibre
Accés restringit per política de l'editorialMachine 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
(2003-02)
Report de recerca
Accés obertIn 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 algorithms: a survey and experimental evaluation
(Institute of Electrical and Electronics Engineers (IEEE), 2002)
Text en actes de congrés
Accés obertIn 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 for microarray gene expression data using simulated annealing guided by the multivariate joint entropy
(2013)
Report de recerca
Accés obertIn 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
Accés obertMicroarray 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)
Capítol de llibre
Accés restringit per política de l'editorialIn 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)
Text en actes de congrés
Accés obertIn 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)
Text en actes de congrés
Accés obertH-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)
Comunicació de congrés
Accés restringit per política de l'editorialThis 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)
Report de recerca
Accés obertIn 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 ... -
Fuzzy heterogeneous neural networks for signal forecasting
(Springer, 1998)
Text en actes de congrés
Accés obertFuzzy heterogeneous neural networks are recently introduced models based on neurons accepting heterogeneous inputs (i.e. mixtures of numerical and non-numerical information possibly with missing data) with either crisp or ... -
Fuzzy heterogeneous neurons for imprecise classification problems
(Wiley, 2000-02)
Article
Accés obertIn 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 ... -
Fuzzy inputs and missing data in similarity-based heterogeneous neural networks
(1998-12)
Report de recerca
Accés obertFuzzy heterogeneous networks are recently introduced feed-forward neural network models composed of neurons of a general class whose inputs and weights are mixtures of continuous variables (crisp and/or fuzzy) with ... -
Fuzzy inputs and missing data in similarity-based heterogeneous neural networks
(Springer, 1999)
Text en actes de congrés
Accés obertFuzzy heterogeneous networks are recently introduced neural network models composed of neurons of a general class whose inputs and weights are mixtures of continuous variables (crisp and/or fuzzy) with discrete quantities, ... -
Gene discovery for facioscapulohumeral muscular dystrophy by machine learning techniques
(2015-12-01)
Article
Accés obertFacioscapulohumeral muscular dystrophy (FSHD) is a neuromuscular disorder that shows a preference for the facial, shoulder and upper arm muscles. FSHD affects about one in 20-400,000 people, and no effective therapeutic ... -
Glucose oxidase biosensor modeling and predictors optimization by machine learning methods
(2016-11-01)
Article
Accés obertBiosensors are small analytical devices incorporating a biological recognition element and a physico-chemical transducer to convert a biological signal into an electrical reading. Nowadays, their technological appeal resides ... -
Handling missing values in kernel methods with application to microbiology data
(2013)
Text en actes de congrés
Accés obertWe discuss several approaches that make possible for kernel methods to deal with missing values. The first two are extended kernels able to handle missing values without data preprocessing methods. Another two methods are ... -
Heterogeneous Kohonen networks
(Springer, 2001)
Text en actes de congrés
Accés obertA large number of practical problems involves elements that are described as a mixture of qualitative and quantitative infomation, and whose description is probably incomplete. The self-organizing map is an effective tool ... -
Heterogeneous neural networks: theory and applications
(Universitat Politècnica de Catalunya, 2000-07-18)
Tesi
Accés obertAquest treball presenta una classe de funcions que serveixen de models neuronals generalitzats per ser usats en xarxes neuronals artificials. Es defineixen com una mesura de similitud que actúa com una definició flexible ... -
HIV drug resistance prediction with weighted categorical kernel functions
(2019-07-30)
Article
Accés obertBackground: Antiretroviral drugs are a very effective therapy against HIV infection. However, the high mutation rate of HIV permits the emergence of variants that can be resistant to the drug treatment. Predicting drug ...
