J
José-Luis Sancho-Gómez
Researcher at Universidad Politécnica de Cartagena
Publications - 67
Citations - 1381
José-Luis Sancho-Gómez is an academic researcher from Universidad Politécnica de Cartagena. The author has contributed to research in topics: Multi-task learning & Artificial neural network. The author has an hindex of 11, co-authored 61 publications receiving 1056 citations. Previous affiliations of José-Luis Sancho-Gómez include Ohio State University & University of Cartagena.
Papers
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Pattern classification with missing data: a review
TL;DR: The aim of this work is to analyze the missing data problem in pattern classification tasks, and to summarize and compare some of the well-known methods used for handling missing values.
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K nearest neighbours with mutual information for simultaneous classification and missing data imputation
TL;DR: This article proposes a novel KNN imputation procedure using a feature-weighted distance metric based on mutual information (MI), which provides a missing data estimation aimed at solving the classification task.
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Fully automatic segmentation of ultrasound common carotid artery images based on machine learning
TL;DR: A fully automatic segmentation technique based on Machine Learning and Statistical Pattern Recognition to measure IMT from ultrasound CCA images is proposed and the concepts of Auto-Encoders (AE) and Deep Learning have been included in the classification strategy.
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Automatic detection of the intima-media thickness in ultrasound images of the common carotid artery using neural networks
Rosa-María Menchón-Lara,María-Consuelo Bastida-Jumilla,Juan Morales-Sánchez,José-Luis Sancho-Gómez +3 more
TL;DR: An effective image segmentation method for the IMT measurement in an automatic way is proposed, which is posed as a pattern recognition problem, and a combination of artificial neural networks has been trained to solve this task.
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Classifying patterns with missing values using Multi-Task Learning perceptrons
TL;DR: This paper proposes an MTL-based method for training and operating a modified Multi-Layer Perceptron (MLP) architecture to work in incomplete data contexts and achieves a balance between both classification and imputation by exploiting the advantages of MTL.