Locally linear approximation for Kernel methods : the Railway Kernel

In this paper we present a new kernel, the Railway Kernel, that works properly for general (nonlinear) classification problems, with the interesting property that acts locally as a linear kernel. In this way, we avoid potential problems due to the use of a general purpose kernel, like the RBF kernel, as the high dimension of the induced feature space. As a consequence, following our methodology the number of support vectors is much lower and, therefore, the generalization capability of the proposed kernel is higher than the obtained using RBF kernels. Experimental work is shown to support the theoretical issues

Representing functional data in reproducing Kernel Hilbert Spaces with applications to clustering and classification

Functional data are difficult to manage for many traditional statistical techniques given their very high (or intrinsically infinite) dimensionality. The reason is that functional data are essentially functions and most algorithms are designed to work with (low) finite-dimensional vectors. Within this context we propose techniques to obtain finitedimensional representations of functional data. The key idea is to consider each functional curve as a point in a general function space and then project these points onto a Reproducing Kernel Hilbert Space with the aid of Regularization theory. In this work we describe the projection method, analyze its theoretical properties and propose a model selection procedure to select appropriate Reproducing Kernel Hilbert spaces to project the functional data.Functional data, Reproducing, Kernel Hilbert Spaces, Regularization theory

Similarity networks for classification: a case study in the Horse Colic problem

This 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 function with the mean of the partial input-weight similarities. The resulting neuron model is capable of dealing directly with variables of potentially different nature (continuous, fuzzy, ordinal, categorical). There is also provision for missing values. The network is trained using a two-stage procedure very similar to that used to train a radial basis function (RBF) neural network. The network is compared to two types of RBF networks in a non-trivial dataset: the Horse Colic problem, taken as a case study and analyzed in detail.Postprint (published version

Broadband transverse susceptibility in multiferroic Y-type hexaferrite Ba0.5Sr1.5Zn2Fe12O22

Producción CientíficaNoncollinear spin systems with magnetically induced ferroelectricity from changes in spiral magnetic ordering have attracted significant interest in recent research due to their remarkable magnetoelectric effects with promising applications. Single phase multiferroics are of great interest for these new multifunctional devices, being Y-type hexaferrites good candidates, and among them the ZnY compounds due to their ordered magnetic behaviour over room temperature. Polycrystalline Y type hexaferrites with composition Ba0.5Sr1.5Zn2Fe2O22 (BSZFO) were sintered in 1050 °C–1250 °C temperature range. Transverse susceptibility measurements carried out on these BSZFO samples in the temperature range 80–350 K with DC fields up to ± 5000 Oe reveal different behaviour depending on the sintering temperature. Sample sintered at 1250 °C is qualitatively different, suggesting a mixed Y and Z phase like CoY hexaferrites. Sintering at lower temperatures produce single phase Y-type, but the transverse susceptibility behaviour of the sample sintered at 1150 °C is shifted at temperatures 15 K higher. Regarding the DC field sweeps the observed behaviour is a peak that shifts to lower values with increasing temperature, and the samples corresponding to single Y phase exhibit several maxima and minima in the 250 K–330 K range at low DC applied field as a result of the magnetic field induced spin transitions in this compound.Ministerio de Ciencia, Innovación y Universidades; Agencia Estatal de Investigación with FEDER (MAT2016-80784-P

“Last-chance” sales: what makes them credible?

This paper analyzes the firms’ standard practice of announcing clearance or “last-chance” sales, namely advertising that a particular product is not going to be available in the market anymore. In the context of a two-period signaling game, prices and advertising decisions of firms are analyzed. Then, the set of separating and pooling equilibria is characterized, so that the above usual advertising techniques can be better understood as equilibria of this model for certain parameter values. In particular, this paper shows that, when the firm which continues in the business knows that few of their current customers will come back in future periods, the set of separating equilibria shrinks. That is, fewer future prospects induce all types of firms to compete for current consumers, leading to pooling equilibria in which all firms announce a “last-chance” sale, even if some of them know they will remain in the industry next period.signaling, advertising, separating equilibria, information transmission

Feature selection for microarray gene expression data using simulated annealing guided by the multivariate joint entropy

In 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. Its low complexity is based on the reuse of previous computations to calculate current feature relevance. The mu-TAFS algorithm --named as such to differentiate it from previous TAFS algorithms-- implements a simulated annealing technique specially designed for feature subset selection. The algorithm is applied to the maximization of gene subset relevance in several public-domain microarray data sets. The experimental results show a notoriously high classification performance and low size subsets formed by biologically meaningful genes.Postprint (published version

Does it pay to be socially responsible? Evidence from Spanish retail banking sector

This paper presents a theoretical and empirical analysis of strategic competition in retail banking when some of the financial firms are non-profit organisations that invest in social activities. Banking literature about competition is fairly large, but the strategic interaction between profit maximizing and non profit maximizers has not been extensively analysed except for Purroy and Salas (1999). In this paper, a completely different approach is taken. An adaptation of Hotelling’s two stage model of spatial competition is developed to take into account consumer perceptions respect to the two different types of financial institutions. The empirical analysis confirms that consumers take into account other features different from the price, such as social contribution or closer service to make a deposit or mortgage decision. These conclusions are of interest in the debate about a firm’s social or ethical activities. It is shown that if consumers value social activities, firms can improve their results by behaving socially responsible.Strategic competition; Hotelling´s model; Spanish banking; Corporate social responsibility

Recursive linear estimation for discrete time systems in the presence of different multiplicative observation noises

This paper describes a design for a least mean square error estimator in discrete time systems where the components of the state vector, in measurement equation, are corrupted by different multiplicative noises in addition to observation noise. We show how known results can be considered a particular case of the algorithm stated in this paperState estimation, multiplicative noise, uncertain observations