Non-linear System Identification with Composite Relevance Vector Machines

Nonlinear system identification based on relevance vector machines (RVMs) has been traditionally addressed by stacking the input and/or output regressors and then performing standard RVM regression. This letter introduces a full family of composite kernels in order to integrate the input and output information in the mapping function efficiently and hence generalize the standard approach. An improved trade-off between accuracy and sparsity is obtained in several benchmark problems. Also, the RVM yields confidence intervals for the predictions, and it is less sensitive to free parameter selectionPublicad

La distribución de recursos entre Comunidades Autónomas. Una primera aproximación a los cambios derivados del nuevo modelo de financiación

El presente trabajo tiene como objetivo analizar el cambio en los resultados distributivos del nuevo modelo de financiación autonómica de régimen común con respecto al vigente desde 2002. Para ello se utilizan indicadores de desigualdad y progresividad derivados de curvas de concentración, así como indicadores de dispersión y de elasticidad. El resultado fundamental que se obtiene es que, a pesar de las modificaciones sustanciales que se producen en la estructura del nuevo modelo, ello no conlleva cambios significativos en los efectos redistributivos de las transferencias, ni en términos de desigualdad ni en términos de progresividad. No obstante, el nuevo modelo sí produce menos reordenación y, en consecuencia, menos desigualdad generada por la misma.The present work has as aim to analyze the change in the distributive results of the new model of financing of Autonomous Communities of common regime with regard to model in force from 2002. For it we use indicators of inequality and progressiveness derived from curves of concentration, as well as indicators of dispersion and of elasticity. The fundamental result obtained is that, in spite of the substantial modifications that are produced in the structure of the new model, it does not carry significant changes in the redistributive effects of the transfers, nor in terms of inequality nor in terms of progressiveness. Nevertheless, the new model produces less reranking and, in consequence, less inequality generated by the same one

Kernel antenna array processing

We introduce two support vector machine (SVM)-based approaches for solving antenna problems such as beamforming, sidelobe suppression, and maximization of the signal-to-noise ratio. A basic introduction to SVM optimization is provided and a complex nonlinear SVM formulation developed to handle antenna array processing in space and time. The new optimization formulation is compared with both the minimum mean square error and the minimum variance distortionless response methods. Several examples are included to show the performance of the new approachesPublicad

Kernel-Based Framework for Multitemporal and Multisource Remote Sensing Data Classification and Change Detection

The multitemporal classification of remote sensing images is a challenging problem, in which the efficient combination of different sources of information (e.g., temporal, contextual, or multisensor) can improve the results. In this paper, we present a general framework based on kernel methods for the integration of heterogeneous sources of information. Using the theoretical principles in this framework, three main contributions are presented. First, a novel family of kernel-based methods for multitemporal classification of remote sensing images is presented. The second contribution is the development of nonlinear kernel classifiers for the well-known difference and ratioing change detection methods by formulating them in an adequate high-dimensional feature space. Finally, the presented methodology allows the integration of contextual information and multisensor images with different levels of nonlinear sophistication. The binary support vector (SV) classifier and the one-class SV domain description classifier are evaluated by using both linear and nonlinear kernel functions. Good performance on synthetic and real multitemporal classification scenarios illustrates the generalization of the framework and the capabilities of the proposed algorithms.Publicad

On the Differential Analysis of Enterprise Valuation Methods as a Guideline for Unlisted Companies Assessment (I): Empowering Discounted Cash Flow Valuation

The Discounted Cash Flow (DCF) method is probably the most extended approach used in company valuation, its main drawbacks being probably the known extreme sensitivity to key variables such asWeighted Average Cost of Capital (WACC) and Free Cash Flow (FCF) estimations not unquestionably obtained. In this paper we propose an unbiased and systematic DCF method which allows us to value private equity by leveraging on stock markets evidences, based on a twofold approach: First, the use of the inverse method assesses the existence of a coherentWACC that positively compares with market observations; second, different FCF forecasting methods are benchmarked and shown to correspond with actual valuations. We use financial historical data including 42 companies in five sectors, extracted from Eikon-Reuters. Our results show that WACC and FCF forecasting are not coherent with market expectations along time, with sectors, or with market regions, when only historical and endogenous variables are taken into account. The best estimates are found when exogenous variables, operational normalization of input space, and data-driven linear techniques are considered (Root Mean Square Error of 6.51). Our method suggests that FCFs and their positive alignment with Market Capitalization and the subordinate enterprise value are the most influencing variables. The fine-tuning of the methods presented here, along with an exhaustive analysis using nonlinear machine-learning techniques, are developed and discussed in the companion paper

Opening the 21st Century Technologies to Industries: On the Special Issue Machine Learning for Society

Machine learning techniques, more commonly known today as artificial intelligence, are playing an increasingly important role in all aspects of our lives. Their applications extend to all areas of society where similar techniques can be accommodated to provide efficient and interesting solutions to a wide range of problems. In this Special Issue entitled Machine Learning for Society [1], we present some examples of the applications of this type of technique. From the valuation of unlisted companies to the characterization of clients, through the detection of financial crises or the prediction of the behavior of the exchange rate, this group of works presented here has in common the search for efficient solutions based on a set of historical data, and the application of artificial intelligence techniques. The techniques and datasets used, as well as the relevant findings developed in the different articles of this Special Issue, are summarized below

Learning non-linear time scales with Kernel γ-Filters

A family of kernel methods, based on the γ-filter structure, is presented for non-linear system identification and time series prediction. The kernel trick allows us to develop the natural non-linear extension of the (linear) support vector machine (SVM) γ-filter, but this approach yields a rigid system model without non-linear cross relation between time-scales. Several functional analysis properties allow us to develop a full, principled family of kernel γ-filters. The improved performance in several application examples suggests that a more appropriate representation of signal states is achieved.Publicad

A new algorithm for rhythm discrimination in cardioverter defibrillators based on the initial voltage changes of the ventricular electrogram

Aims: Ventricular activation onset is faster in supraventricular beats than in ventricular rhythms. The aim of this study was to evaluate a criterion to differentiate supraventricular (SVT) from ventricular tachycardia (VT) based on the analysis of the initial voltage changes in ICD-stored morphology electrograms. Methods. Far field ICD-stored EGMs were obtained from 68 VT and 38 SVT episodes in 16 patients. The first EGM peak was detected, three consecutive time epochs were defined within the preceding 80 ms window and the voltage changes with respect to a sinus template were analysed during each time period and combined into a single parameter for rhythm discrimination. Results. The algorithm was tested in an independent validation group of 442 VT and 97 SVT spontaneous episodes obtained from 22 patients with a dual chamber ICD. The area under the receiver-operator characteristics (ROC) curve indicated that the arrhythmia separability with this method was 0.95 (tolerance interval: 0.85-0.99) and 0.98 (0.87-0.99) for the control and validation groups respectively. A specificity of 0.91 was obtained at 95% sensitivity in the validation group. Conclusion. The analysis of voltage changes during the initial ventricular activation process is feasible using the far field stored electrograms of an ICD system and yields a high sensitivity and specificity for arrhythmia discrimination

A robust support vector algorithm for nonparametric spectral analysis

A new approach to the nonparametric spectral estimation on the basis of the support vector method (SVM) framework is presented. A reweighted least squared error formulation avoids the computational limitations of quadratic programming. The application to a synthetic example and to a digital communication problem shows the robustness of the SVM spectral analysis algorithm.Publicad