Papers by Marcelo Frank Aparco Espinoza
2004 43rd IEEE Conference on Decision and Control (CDC) (IEEE Cat. No.04CH37601), 2004
Abstract Within the context of nonlinear system identification, the LS-SVM formulation is extende... more Abstract Within the context of nonlinear system identification, the LS-SVM formulation is extended to define a partially linear LS-SVM in order to identify a model containing a linear part and a nonlinear component. For a given kernel, a unique solution exists when the ...
IFAC Proceedings Volumes, 2003
IFAC Proceedings Volumes, 2004
Within the context of nonlinear system identification, different variants of LS-SVM are applied t... more Within the context of nonlinear system identification, different variants of LS-SVM are applied to the Silver Box dataset. Starting from the dual representation of the LS-SVM, and using Nystr6m techniques, it is possible to compute an approximation for the nonlinear mapping to be used in the primal space. In this way, primal space based techniques as Ordinary Least Squares (OLS), Ridge Regression (RR) and Partial Least Squares (PLS) are applied to the same dataset together with the dual version of LS-SVM. We obtain root mean squared error (RMSE) values of the order of 10-4 using iterative prediction on a pre-defined test set.
2003 IEEE International Conference on Computational Intelligence for Financial Engineering, 2003. Proceedings.
Abstract Classification algorithms like linear discriminant analysis and logistic regression are ... more Abstract Classification algorithms like linear discriminant analysis and logistic regression are popular linear techniques for modelling and predicting corporate distress. These techniques aim at finding an optimal linear combination of explanatory input variables, such as, eg, ...
Lecture Notes in Computer Science, 2009
An application of multiway spectral clustering with out-of-sample extensions towards clustering t... more An application of multiway spectral clustering with out-of-sample extensions towards clustering time series is presented. The data correspond to power load time series acquired from substations in the Belgian grid for a period of 5 years. Spectral clustering methods ...
Lecture Notes in Computer Science, 2005
Based on the Nyström approximation and the primal-dual formulation of Least Squares Support Vecto... more Based on the Nyström approximation and the primal-dual formulation of Least Squares Support Vector Machines (LS-SVM), it becomes possible to apply a nonlinear model to a large scale regression problem. This is done by using a sparse approximation of the nonlinear mapping induced by the kernel matrix, with an active selection of support vectors based on quadratic Renyi entropy criteria. The methodology is applied to the case of load forecasting as an example of a real-life large scale problem in industry, for the case of 24-hours ahead predictions. The results are reported for different number of initial support vectors, which cover between 1% and 4% of the entire sample, with satisfactory results.
Physiological Measurement, 2006
In this paper we apply system identification techniques in order to build a model suitable for th... more In this paper we apply system identification techniques in order to build a model suitable for the prediction of glycemia levels of critically ill patients admitted to the intensive care unit. These patients typically show increased glycemia levels, and it has been shown that glycemia control by means of insulin therapy significantly reduces morbidity and mortality. Based on a real-life dataset from 15 critically ill patients, an initial input-output model is estimated which captures the insulin effect on glycemia under different settings. To incorporate patient-specific features, an adaptive modeling strategy is also proposed in which the model is re-estimated at each time step (i.e., every hour). Both one-hour-ahead predictions and four-hours-ahead simulations are executed. The optimized adaptive modeling technique outperforms the general initial model. To avoid data selection bias, 500 permutations, in which the patients are randomly selected, are considered. The results are satisfactory both in terms of forecasting ability and in the clinical interpretation of the estimated coefficients.
Neural Processing Letters, 2005
This paper considers the estimation of monotone nonlinear regression based on Support Vector Mach... more This paper considers the estimation of monotone nonlinear regression based on Support Vector Machines (SVMs), Least Squares SVMs (LS-SVMs) and kernel machines. It illustrates how to employ the primal-dual optimization framework characterizing (LS-)SVMs in order to derive a globally optimal one-stage algorithm. As an practical application, this letter considers the smooth estimation of the cumulative distribution functions (cdf) which leads to a kernel regressor that incorporates a Kolmogorov-Smirnoff discrepancy measure, a Tikhonov based regularization scheme and a monotonicity constraint.
Journal of Forecasting, 2006
The use of linear error correction models based on stationarity and cointegration analysis, typic... more The use of linear error correction models based on stationarity and cointegration analysis, typically estimated with least squares regression, is a common technique for financial time series prediction. In this paper, the same formulation is extended to a nonlinear error correction model using the idea of a kernelbased implicit nonlinear mapping to a high-dimensional feature space in which linear model formulations are specified. Practical expressions for the nonlinear regression are obtained in terms of the positive definite kernel function by solving a linear system. The nonlinear least squares support vector machine model is designed within the Bayesian evidence framework that allows us to find appropriate trade-offs between model complexity and in-sample model accuracy. From straightforward primal-dual reasoning, the Bayesian framework allows us to derive error bars on the prediction in a similar way as for linear models and to perform hyperparameter and input selection. Starting from the results of the linear modelling analysis, the Bayesian kernel-based prediction is successfully applied to out-of-sample prediction of an aggregated equity price index for the European chemical sector.
Journal of Diabetes Science and Technology, 2007
Background: Strict blood glucose control by applying nurse-driven protocols is common nowadays in... more Background: Strict blood glucose control by applying nurse-driven protocols is common nowadays in intensive care units (ICUs). Implementation of a predictive control system can potentially reduce the workload for medical staff but requires a model for accurately predicting the glycemia signal within a certain time horizon. Methods: GlucoDay (A. Menarini Diagnostics, Italy) data coming from 19 critically ill patients (from a surgical ICU) are used to estimate the initial ICU “minimal” model (based on data of the first 24 hours) and to reestimate the model as new measurements are obtained. The reestimation is performed every hour or every 4 hours. For both approaches the optimal size of the data set for each reestimation is determined. Results: The prediction error that is obtained when applying the 1-hour reestimation strategy is significantly smaller than when the model is reestimated only every 4 hours (p < 0.001). The optimal size of the data set to be considered in each reesti...
IEEE Transactions on Automatic Control, 2005
In this note, we propose partially linear models with least squares support vector machines (LS-S... more In this note, we propose partially linear models with least squares support vector machines (LS-SVMs) for nonlinear ARX models. We illustrate how full black-box models can be improved when prior information about model structure is available. A real-life example, based on the Silverbox benchmark data, shows significant improvements in the generalization ability of the structured model with respect to the full black-box model, reflected also by a reduction in the effective number of parameters.
Computational Management Science, 2006
Based on the Nyström approximation and the primal-dual formulation of the least squares support v... more Based on the Nyström approximation and the primal-dual formulation of the least squares support vector machines, it becomes possible to apply a nonlinear model to a large scale regression problem. This is done by using a sparse approximation of the nonlinear mapping induced by the kernel matrix, with an active selection of support vectors based on quadratic Renyi entropy criteria. The methodology is applied to the case of load forecasting as an example of a real-life large scale problem in industry. The forecasting performance, over ten different load series, shows satisfactory results when the sparse representation is built with less than 3% of the available sample.
... MISC{Espinoza04modelstructure, author = {Marcelo Espinoza and Johan AK Suykens and Bart De Mo... more ... MISC{Espinoza04modelstructure, author = {Marcelo Espinoza and Johan AK Suykens and Bart De Moor}, title = {Model ... 65, Prediction Risk and Architecture Selection for Neural Networks Moody - 1994. 55, Kernel Smoothing in Partial Linear Models Speckman - 1988. ...
un sistema de riego movil por goteo, calculado para cultivos hortícolas, el cual se podrá traslad... more un sistema de riego movil por goteo, calculado para cultivos hortícolas, el cual se podrá trasladar sin necesidad de un gasto excesivo en sistemas de riego fijos, para lo cual se montara en una plataforma el sistema de riego, es decir los filtros, bomba, y otros implementos los que serán transportados por medio de un tractor agrícola o algún otro vehículo con capacidad de tiro. En la Tesis se desarrolla un sistema del riego por goteo, se hace el diseño hidráulico y el diseño del remolque para la transportación del sistema, cálculos que servirán para su construcción posterior en caso de necesitarlo.
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Papers by Marcelo Frank Aparco Espinoza