Allende , Héctor

In recent years several models for financial high-frequency data have been proposed. One of the most known models for this type of applications is the ACM-ACD model. This model focuses on modelling the underlying joint distribution of both duration and price changes between consecutive transactions. However this model imposes distributional assumptions and its number of parameters increases rapidly (producing a complex and slow adjustment process). Therefore, we propose using two machine learning models, that will work sequentially, based on the ACM-ACD model. The results show a comparable performance, achieving a better performance in some cases. Also the proposal achieves a significatively more rapid convergence. The proposal is validated with a well-known financial data set.

The architectural design of neuro-fuzzy models is one of the major concern in many important applications. In this work we propose an extension to Rogers’s ANFIS model by providing it with a selforganizing mechanism. The main purpose of this mechanism is to adapt the architecture during the training process by identifying the optimal number of premises and consequents needed to satisfy a user’s performance criterion. Using both synthetic and real data, our proposal yields remarkable results compared to the classical ANFIS.

This work presents a new method for feature extraction of iris images to improve the identification process. The valuable information of the iris is intrinsically located in its natural texture, and preserving and extracting the most relevant features is of paramount importance. The technique consists in several steps from adquisition up to the person identification. Our contribution consists in a multimodal algorithm where a fragmentation of the normalized iris image is performed and, afterwards, regional statistical descriptors with Self-Organizing-Maps are extracted. By means of a biometric fusion of the resulting descriptors, the features of the iris are compared and classified. The results with the iris data set obtained from the Bath University repository show an excellent accuracy reaching up to 99.867%.

In this paper we explore the use of the cluster analysis in segmentation problems, that is, identifying image points with an indication of the region or class they belong to. The proposed algorithm uses the well known agglomerative hierarchical cluster analysis algorithm in order to form clusters of pixels, but modified so as to cope with the high dimensionality of the problem. The results of different stages of the algorithm are saved, thus retaining a collection of segmented images ordered by degree of segmentation. This allows the user to view the whole collection and choose the one that suits him best for his particular application.

In this paper we present a method to detect edges in images. The method consists of using a 3x3 pixel mask to scan the image, moving it from left to right and from top to bottom, one pixel at a time. Each time it is placed on the image, an agglomerative hierarchical cluster analysis is applied to the eight outer pixels. When there is more than one cluster, it means that window is on an edge, and the central pixel is marked as an edge point. After scanning all the image, we obtain a new image showing the marked pixels around the existing edges of the image. Then a thinning algorithm is applied so that the edges are well defined. The method results to be particularly efficient when the image is contaminated. In those cases, a previous restoration method is applied.

The Self Organizing Map (SOM) model is an unsupervised learning neural network that has been successfully applied as a data mining tool. The advantages of the SOMs are that they preserve the topology of the data space, they project high dimensional data to a lower dimension representation scheme, and are able to find similarities in the data. However, the learning algorithm of the SOM is sensitive to the presence of noise and outliers as we will show in this paper. Due to the influence of the outliers in the learning process, some neurons (prototypes) of the ordered map get located far from the majority of data, and therefore, the network will not effectively represent the topological structure of the data under study. In this paper, we propose a variant to the learning algorithm that is robust under the presence of outliers in the data by being resistant to these deviations. We call this algorithm Robust SOM (RSOM). We will illustrate our technique on synthetic and real data sets.

The precise knowledge of the statistical properties of synthetic aperture radar (SAR) data plays a central role in image processing and understanding. These properties can be used for discriminating types of land uses and to develop specialized filters for speckle noise reduction, among other applications. In this work we assume the distribution G0 A as the universal model for multilook amplitude SAR images under the multiplicative model. We study some important properties of this distribution and some classical estimators for its parameters, such as Maximum Likelihood (ML) estimators, but they can be highly influenced by small percentages of ‘outliers’, i.e., observations that do not fully obey the basic assumptions. Hence, it is important to find Robust Estimators. One of the best known classes of robust techniques is that of M estimators, which are an extension of the ML estimation method. We compare those estimation procedures by means of a Monte Carlo experiment.

In this paper, we study a single objective extension of support vector machines for multicategory classification. Extending the dual formulation of binary SVMs, the algorithm looks for minimizing the sum of all the pairwise distances among a set of prototypes, each one constrained to one of the convex-hulls enclosing a class of examples. The final discriminant system is built looking for an appropriate reference point in the feature space. The obtained method preserves the form and complexity of the binary case, optimizing just one convex objective function with m variables and 2m+K constraints, where m is the number of examples and K the number of classes. Non-linear extension are straightforward using kernels while soft margin versions can be obtained by using reduced convex hulls. Experimental results in well-known UCI benchmarks are presented, comparing the accuracy and efficiency of the proposed approach with other state-of-the-art methods.

The Neural Gas (NG) is a Vector Quantization technique where a set of prototypes self organize to represent the topology structure of the data. The learning algorithm of the Neural Gas consists in the estimation of the prototypes location in the feature space based in the stochastic gradient descent of an Energy function. In this paper we show that when deviations from idealized distribution function assumptions occur, the behavior of the Neural Gas model can be drastically affected and will not preserve the topology of the feature space as desired. In particular, we show that the learning algorithm of the NG is sensitive to the presence of outliers due to their influence over the adaptation step. We incorporate a robust strategy to the learning algorithm based on M-estimators where the influence of outlying observations are bounded. Finally we make a comparative study of several estimators where we show the superior performance of our proposed method over the original NG, in static data clustering tasks on both synthetic and real data sets.

Semi-Supervised Learning is one of the most popular and emerging issues in Machine Learning. Since it is very costly to label large amounts of data, it is useful to use data sets without labels. For doing that, normally we uses Semi-Supervised Learning to improve the performance or efficiency of the classification algorithms. This paper intends to use the techniques of Semi-Supervised Learning to boost the performance of the Robust Alternating AdaBoost algorithm. We introduce the algorithm RADA+ and compare it with RADA, re- porting the performance results using synthetic and real data sets, the latter obtained from a benchmark site.