Catálogo de publicaciones - libros

The identification of -regulatory binding sites in DNA is a difficult problem in computational biology. To obtain a full understanding of the complex machinery embodied in genetic regulatory networks it is necessary to know both the identity of the regulatory transcription factors together with the location of their binding sites in the genome. We show that using an SVM together with data sampling, to integrate the results of individual algorithms specialised for the prediction of binding site locations, can produce significant improvements upon the original algorithms. These results make more tractable the expensive experimental procedure of actually verifying the predictions.

DNA Microarray technology allows us to monitor the expression level of thousands of genes simultaneously. This technique has become a relevant tool to identify different types of cancer.

Several machine learning techniques such as the Support Vector Machines (SVM) have been proposed to this aim. However, common SVM algorithms are based on Euclidean distances which do not reflect accurately the proximities among the sample profiles. The SVM has been extended to work with non-Euclidean dissimilarities. However, no dissimilarity can be considered superior to the others because each one reflects different features of the data.

In this paper, we propose to combine several Support Vector Machines that are based on different dissimilarities to improve the performance of classifiers based on a single measure. The experimental results suggest that our method reduces the misclassification errors of classifiers based on a single dissimilarity and a widely used combination strategy such as Bagging.

This paper presents a locally recurrent globally feedforward fuzzy neural network, with internal feedback, that performs the task of separation of lung sounds, obtained from patients with pulmonary pathology. The filter is a novel generalized Takagi-Sugeno-Kang fuzzy model, where the consequent parts of the fuzzy rules are Block-Diagonal Recurrent Neural Networks. Extensive experimental results, regarding the lung sound category of squawks, are given, and a performance comparison with a series of other fuzzy and neural filters is conducted, underlining the separation capabilities of the proposed filter.

In order to understand the general principles along which sensory processing is organized, several recent studies optimized particular coding objectives on natural inputs for different modalities. The homogeneity of neocortex indicates that a sensitive objective should be able to explain response properties of different sensory modalities. The temporal stability objective was successfully applied to somatosensory and visual processing. We investigate if this objective can also be applied to auditory processing and serves as a general optimization objective for sensory processing. In case of audition, this translates to a set of non-linear complex filters optimized for temporal stability on natural sounds. We show that following this approach we can develop filters that are localized in frequency and time and extract the frequency content of the sound wave. A subset of these filters respond invariant to the phase of the sound. A comparison of the tuning of these filters to the tuning of cat auditory nerves shows a close match. This suggests that temporal stability can be seen as a general objective describing somatosensory, visual and auditory processing.

In this paper we compare thirteen different methods to obtain multi-class probability estimates in view of two medical case studies. The basic classification method used to implement all methods are least squares support vector machine (LS-SVM) classifiers. Results indicate that multi-class kernel logistic regression performs very well, together with a method based on ensembles of nested dichotomies. Also, a Bayesian LS-SVM method imposing sparseness performed very well for methods that combine binary probabilities into multi-class probabilities.

In this paper we consider the question of whether it is possible to classify n-back EEG data into different memory loads across subjects. To capture relevant information from the EEG signal we use three types of features: power spectrum, conditional entropy, and conditional mutual information. In order to reduce irrelevant and misleading features we use a feature selection method that maximizes mutual information between features and classes and minimizes redundancy among features. Using a selected group of features we show that all classifiers can successfully generalize to the new subject for bands 1-40Hz and 1-60Hz. The classification rates are statistically significant and the best classification rates, close to 90%, are obtained using conditional entropy features.

As a causality criterion we propose the conditional relative entropy. The relationship with information theoretic functionals mutual information and entropy is established. The conditional relative entropy criterion is compared with 3 well-established techniques for causality detection: ‘Sims‘, ‘Geweke- Meese-Dent‘ and ‘Granger‘. It is shown that the conditional relative entropy, as opposed to these 3 criteria, is sensitive to0. non-linear causal relationships. All results are illustrated on real-world time series of human gait.

Finding a template image in another larger image is a problem that has applications in many vision research areas such as models for object detection and tracking. The main problem here is that under real-world conditions the searched image usually is a deformed version of the template, so that these deformations have to be taken into account by the matching procedure. A common way to do this is by minimizing the difference between the template and patches of the search image assuming that the template can undergo 2D affine transformations. A popular differential algorithm for achieving this has been proposed by Lucas and Kanade [1], with the disadvantage that it works only for small transformations. Here we investigate the transformation properties of a differential template matching approach by using resolution pyramids in combination with transformation pyramids, and show how we can do template matching under large-scale transformations, with simulation results indicating that the scale and rotation ranges can be doubled using a 3 stage pyramid.

In this paper, we discuss fuzzy classifiers based on Kernel Discriminant Analysis (KDA) for two-class problems. In our method, first we employ KDA to the given training data and calculate the component that maximally separates two classes in the feature space. Then, in the one-dimensional space obtained by KDA, we generate fuzzy rules with one-dimensional membership functions and tune the slopes and bias terms based on the same training algorithm as that of linear SVMs. Through the computer experiments for two-class problems, we show that the performance of the proposed classifier is comparable to that of SVMs, and we can easily and visually analyze its behavior using the degrees of membership functions.

Within the taxonomy of feature extraction methods, recently the Wrapper approaches lost some popularity due to the associated computational burden, compared to Embedded or Filter methods. The dominating factor in terms of computational costs is the number of adaption cycles used to train the black box classifier or function approximator, e.g. a Multi Layer Perceptron. To keep a wrapper approach feasible, the number of adaption cycles has to be minimized, without increasing the risk of missing important feature subset combinations.

We propose a search strategy, that exploits the interesting properties of Chow-Liu trees to reduce the number of considered subsets significantly. Our approach restricts the candidate set of possible new features in a forward selection step to children from certain tree nodes. We compare our algorithm with some basic and well known approaches for feature subset selection. The results obtained demonstrate the efficiency and effectiveness of our method.