A close examination of the validity of the Markovian approximation in the context of relaxat~on t?eory is presented. In particular, we examine the question of positivity of various approxImations to the reduced dynamics of an open system in interaction with a heat r.es~rvoir. It is show~ that the Markovian equations of motion obtained in the weak coupling lImIt .(Redfield equatIons) are a consistent approximation to the actual reduced dynamics only If supplemented by a slippage in the initial conditions. This slippage captures the effects of the non-Markovian evolution that takes place in a short transient time, of the order of the relaxation time of the isolated bath.

Esta es la versión de autor del artículo publicado en: This is an author produced version of a paper published in: computationally more costly methods that directly select optimal or near-optimal subensembles.

ÐA fuzzy decision tree is constructed by allowing the possibility of partial membership of a point in the nodes that make up the tree structure. This extension of its expressive capabilities transforms the decision tree into a powerful functional approximant that incorporates features of connectionist methods, while remaining easily interpretable. Fuzzification is achieved by superimposing a fuzzy structure over the skeleton of a CART decision tree. A training rule for fuzzy trees, similar to backpropagation in neural networks, is designed. This rule corresponds to a global optimization algorithm that fixes the parameters of the fuzzy splits. The method developed for the automatic generation of fuzzy decision trees is applied to both classification and regression problems. In regression problems, it is seen that the continuity constraint imposed by the function representation of the fuzzy tree leads to substantial improvements in the quality of the regression and limits the tendency to overfitting. In classification, fuzzification provides a means of uncovering the structure of the probability distribution for the classification errors in attribute space. This allows the identification of regions for which the error rate of the tree is significantly lower than the average error rate, sometimes even below the Bayes misclassification rate.

Esta es la versión de autor de la comunicación de congreso publicada en: This is an author produced version of a paper published in: AbstractWe present a novel ensemble pruning method based on reordering the classifiers obtained from bagging and then selecting a subset for aggregation. Ordering the classifiers generated in bagging makes it possible to build subensembles of increasing size by including first those classifiers that are expected to perform best when aggregated. Ensemble pruning is achieved by halting the aggregation process before all the classifiers generated are included into the ensemble. Pruned subensembles containing between 15% and 30% of the initial pool of classifiers, besides being smaller, improve the generalization performance of the full bagging ensemble in the classification problems investigated.

No abstract

Index tracking consists in reproducing the performance of a stock-market index by investing in a subset of the stocks included in the index. A hybrid strategy that combines an evolutionary algorithm with quadratic programming is designed to solve this NP-hard problem: Given a subset of assets, quadratic programming yields the optimal tracking portfolio that invests only in the selected assets. The combinatorial problem of identifying the appropriate assets is solved by a genetic algorithm that uses the output of the quadratic optimization as fitness function. This hybrid approach allows the identification of quasi-optimal tracking portfolios at a reduced computational cost.

An expectation propagation (EP) algorithm is proposed for approximate inference in linear regression models with spike-and-slab priors. This EP method is applied to regression tasks in which the number of training instances is small and the number of dimensions of the feature space is large. The problems analyzed include the reconstruction of genetic networks, the recovery of sparse signals, the prediction of user sentiment from customer-written reviews and the analysis of biscuit dough constituents from NIR spectra. The proposed EP method outperforms in most of these tasks another EP method that ignores correlations in the posterior and a variational Bayes technique for approximate inference. Additionally, the solutions generated by EP are very close to those given by Gibbs sampling, which can be taken as the gold standard but can be much more computationally expensive. In the tasks analyzed, spikeand-slab priors generally outperform other sparsifying priors, such as Laplace, Student's t and horseshoe priors. The key to the improved predictions with respect to Laplace and Student's t priors is the superior selective shrinkage capacity of the spike-and-slab prior distribution.

A ~icroscopic description for reactions in condensed media involving hydrogen tunneling, valtd ove~ a large temperature range, is presented. The tunneling system, represented by a pseudospm (S = 1/2), reaches eqUilibrium when coupled to its environment, modeled by a collection of harmonic oscillators that behave like a heat bath. The environment includes both modes of the lattice (or solvent) and those molecular vibrations which play an active role in the tunneling process. Analytical expressions for the reaction rate are given in various regimes. L form Rform FIG. 1. Tautomerization reaction for malonaldehyde.