Nonlinear multiclass discriminant analysis

An alternative nonlinear multiclass discriminant algorithm is presented.This algorithm is based on the use of kernel functions and is designed to optimize a general linear discriminant analysis criterion based on scatter matrices.By reformulating these matrices in a specific form,a straightforward derivation a lows the kernel function to be introduced in a simple and direct way.Moreover,we propose a method to determine the value of the regularization parameter,based on this derivation.This work was supported in part by the HPCMO PET program and by the Spanish Ministry of Science and Technolog

Sistema para la detección automática de señales acústicas de banda ancha emitidas por mamiferos marinos

This paper presents the design and preliminary implementation of a computer module for the automatic detection and identification of broadband transient signals emitted by marine mammals generically referred to as clicks. This module will make part of a more wide system designed for detecting and identifying the whole range of signals emitted by marine mammals. The computer module has been extensively tested with simulated signals incorporating the basic structure of sperm whale clicks and with real signals covering a broad spectrum of situations encountered at sea as this corresponding to the presence of high level interfering noise and cluttering of signals

Pattern classification with missing values using multitask learning

In many real-life applications it is important to know how to deal with missing data (incomplete feature vectors). The ability of handling missing data has become a fundamental requirement for pattern classification because inappropriate treatment of missing data may cause large errors or false results on classification. A novel effective neural network is proposed to handle missing values in incomplete patterns with Multitask Learning (MTL). In our approach, a MTL neural network learns in parallel the classification task and the different tasks associated to incomplete features. During the MTL process, missing values are estimated or imputed. Missing data imputation is guided and oriented by the classification task, i.e., imputed values are those that contribute to improve the learning. We prove the robustness of this MTL neural network for handling missing values in classification problems from UCI database.This work will stimulate future works in many directions. Some of them are using different error functions (crossentropy error in discrete tasks, and sum-of-squares error in continuous tasks), adding an EM-model to probability density estimation into the proposed MTL scheme, setting the number of neurons in each subnetwork dynamically using constructive learning, an extensive comparison with other imputation methods, to use this procedure in regression problems, and extending the proposed method to different machines, e.g., Support Vector Machines (SVM)

Red neural multitarea para problemas de decisión con información incompleta

Missing data is a common problem that appears in many real applications. Handling missing data is a essential requirement for pattern classification because inappropriate treatment of missing data may cause large errors or false results on classification. A classical approach is to estimate and fill the missing values. Up to now, proposed methods are efficient but they do not focus the missing data estimation to pattern classification. In this work, we propose a novel neural network that uses the advantages of Multitask Learning (MTL) to classify patterns with incomplete data. A MTL neural network learns at the same time the classification task and the different task associated to incomplete features. Missing data estimation is guided and oriented by the classification task during the MTL process. Obtained results based on real and artificial classification problems demonstrate the advantages of the proposed algorithm

Imputación de datos incompletos y clasificación de patrones mediante aprendizaje multitarea

Almost all research on supervised learning is based on the assumption that training data are completely observable, but it is not a common situation because real world databases are rarely complete. The ability of handling missing data has become a fundamental requirement for machine learning. Up to now, proposed methods consider the problem as two separated tasks, main task and imputation task, and solve them separately (Single Task Learning, STL). In this paper, a new effective method is proposed to handle missing features in incomplete databases with Multitask Learning (MTL). This approach uses the imputation task as extra task and learning in parallel with the main task. Thus, imputation is guided and oriented by the learning process, i.e., imputed values are those that contribute to improve the learning. In this paper we use the advantages of MTL to handling missing data and analyze its robustness for handling different missing variables in real an artificial data sets.Este trabajo está parcialmente financiado por el Ministerio de Educación y Ciencia a través del proyecto TIC2002-03033

Multiple feature models for image matching

The common approach to image matching is to detect spatial features present in both images and create a mapping that relates both images. The main draw back of this method takes place when more than one matching is likely. A first simplification to this ambiguity is to represent with apara-metric model the point locus where the matching is highly likely,and then use a POCS(projection on to convex sets)procedure combined with Tikhonov regularization that results in the mapping vectors. However,if there is more than one model perpixel,the regularization and constrainforcing process faces a multiplechoice dilemma that has no easy solution. This work proposes a frame work to overcome this draw back: the combined projection over multiple models base don the norm of the projection–pointdis-tance. This approach is tested on a stereo-pair that presents multiple choices of similar likelihood.This work is partially supported by the Spanish Ministerio de Ciencia y Tecnología,under grant TIC2002-03033

Estimación de densidad de probabilidad mediante ventanas de Parzen

Este trabajo presenta la estimación de funciones de densidad de probabilidad mediante ventanas de Parzen, que constituye una de las técnicas no-paramétricas más extendidas en este campo. Se analizan experimentalmente sus capacidades en un problema de procesado de imagenAsociación de Jóvenes Investigadores de Cartagena, (AJICT). Universidad Politécnica de Cartagena. Escuela Técnica Superior de Ingeniería Industrial UPCT, (ETSII). Escuela Técnica Superior de Ingeniería Agronómica, (ETSIA), Escuela Técnica Superior de Ingeniería de Telecomunicación (ETSIT). Escuela de Ingeniería de Caminos, Canales, y Puertos y de Ingeniería de Minas, (EICM). Fundación Séneca, Agencia Regional de Ciencia y Tecnología. Parque Tecnológico de Fuente Álamo. Grupo Aquilin

Improving hand gestures recognition capabilities by ensembling convolutional networks

Hand gestures provide humans a convenient way to interact with computers and many applications. However, factors such as the complexity of hand gesture models, differences in hand size and position, and other factors can affect the performance of the recognition and classification algorithms. Some developments of deep learning such as Convolutional Neural Networks (CNN) and Capsule Networks (CapsNets) have been proposed to improve the performance of image recognition systems in this particular field. While CNNs are undoubtedly the most widely used networks for object detection and image classification, CapsNets emerged to solve part of the limitations of the former. For this reason, in this work a particular ensemble of both networks is proposed to solve the American Sign Language recognition problem very effectively. The method is based on increasing diversity in both the model and the dataset. The results obtained show that the proposed ensemble model together with a simple data augmentation process produces a very competitive accuracy performance with the all considered datasets.This work has been partially supported by Instituto de Salud Carlos III (Project PI17/00771, Ministerio de Economía y de Competitividad, Government of Spain) and Fundación Séneca (Project 20901/PI/18, Agencia de Ciencia y Tecnología, Murcia)

Aprendizaje multitarea en problemas con un número reducido de datos

MultiTask Learning (MTL) is a procedure to train a neural network to learn several related tasks simultaneously considering one of them as main task and the others as secondary tasks. In this paper, we have tested a method to obtain artificially tasks which are related with the main one, because in many real cases, knowledge about problem to be solved is uncertain. We use sample selection techniques to generate related tasks with the main one, in particular, samples close the classification boundary. Moreover, a new procedure to train MultiLayer Perceptrons with generated tasks is described.Este trabajo está subvencionado por el Ministerio de Educación y Ciencia, otorgado por TIC2002-03033

Bilingual education and school choice: a case study of public secondary schools in the Spanish region of Madrid

In the academic year of 2004-2005 the Spanish region of Madrid began to implement a bilingual educational programme in public schools. Currently, 45% of the public educational system (primary and secondary) participates in the bilingual programme of the Community of Madrid (hereinafter MBP). One of the objectives sought by this programme, but not the only one, is to make the study of a foreign language accessible to students from economically less favoured families (who have greater difficulty in meeting the cost of private language tutoring). Consequently, our study aims to analyse whether, as proposed, students from disadvantaged socioeconomic backgrounds effectively participate in the MBP. To comply with this objective, we estimate a model directed at identifying which factors influence the selection of a bilingual public school by families. The results obtained reveal that the MBP has led to the sorting of students by socioeconomic and cultural status, causing cream skimming within the public education sector in Madrid. This is due to the influence in the choice of a bilingual public school of factors such as the educational level and the mother’s immigrant status, the occupational level of the parents and the cultural capital of the household