Catálogo de publicaciones - libros

Recent neurocognitive researches demonstrate how the natural processing of auditory sentences occurs. Nowadays, there is not an appropriate human-computer speech interaction, and this constitutes a computational challenge to be overtaked. In this direction, we propose a speech comprehension software architecture to represent the flow of this neurocognitive model. In this architecture, the first step is the speech signal processing to written words and prosody coding. Afterwards, this coding is used as input in syntactic and prosodic-semantic analyses. Both analyses are done concomitantly and their outputs are matched to verify the best result. The computational implementation applies wavelets transforms to speech signal codification and data prosodic extraction and connectionist models to syntactic parsing and prosodic-semantic mapping.

In time series prediction problems in which the current series presents a certain seasonality, the long term and short term prediction capabilities of a learned model can be improved by considering that seasonality as additional information within it. Kernel methods and specifically LS-SVM are receiving increasing attention in the last years thanks to many interesting properties; among them, these type of models can include any additional information by simply adding new variables to the problem, without increasing the computational cost. This work evaluates how including the seasonal information of a series in a designed recursive model might not only upgrade the performance of the predictor, but also allows to diminish the number of input variables needed to perform the modelling, thus being able to increase both the generalization and interpretability capabilities of the model.

Testing the validity of the Efficient Market Hypothesis (EMH) has been an unsolved argument for the investment community. The EMH states that the current market price incorporates all the information available, which leads to a conclusion that given the information available, no prediction of the future price changes can be made. On the other hand, technical analysis, which is essentially the search for recurrent and predictable patterns in asset prices, attempts to forecast future price changes. To the extend that the total return of a technical trading strategy can be regarded as a measure of predictability, technical analysis can be seen as a test of the EMH and in particular of the independent increments version of random walk. This paper is an initial attempt on creating an automated process, based on a combination of a rule-based system and a neural network, of recognizing one of the most common and reliable patterns in technical analysis, the head and shoulders pattern. The systematic application of this automated process on the identification of the head and shoulders pattern and the subsequent analysis of price behavior, in various markets can in principle work as a test of the EMH.

In this paper we discuss sparse least squares support vector regressors (sparse LS SVRs) defined in the reduced empirical feature space, which is a subspace of mapped training data. Namely, we define an LS SVR in the primal form in the empirical feature space, which results in solving a set of linear equations. The independent components in the empirical feature space are obtained by deleting dependent components during the Cholesky factorization of the kernel matrix. The independent components are associated with support vectors and controlling the threshold of the Cholesky factorization we obtain a sparse LS SVM. For linear kernels the number of support vectors is the number of input variables at most and if we use the input axes as support vectors, the primal and dual forms are equivalent. By computer experiments we show that we can reduce the number of support vectors without deteriorating the generalization ability.

Content-based image retrieval (CBIR) is an important and widely studied topic since it can have significant impact on multimedia information retrieval. Recently, support vector machine (SVM) has been applied to the problem of CBIR. The SVM-based method has been compared with other methods such as neural network (NN) and logistic regression, and has shown good results. Genetic algorithm (GA) has been increasingly applied in conjunction with other AI techniques. However, few studies have dealt with the combining GA and SVM, though there is a great potential for useful applications in this area. This paper focuses on simultaneously optimizing the parameters and feature subset selection for SVM without degrading the SVM classification accuracy by combining GA for CBIR. In this study, we show that the proposed approach outperforms the image classification problem for CBIR. Compared with NN and pure SVM, the proposed approach significantly improves the classification accuracy and has fewer input features for SVM.

In this paper, we describe a feature extraction method: Global and Local Preserving Projection (GLPP). GLPP is based on PCA and the recently proposed Locality Preserving Projection (LPP) method. LPP can preserve local information, while GLPP can preserve both global and local information. In this paper we investigate the potential of using GLPP for image categorization. More specifically, we experiment on palmprint images. Palmprint image has been attracting more and more attentions in the image categorization/recognition area in recent years. Experiment is based on benchmark dataset PolyU, using Error Rate as performance measure. Comparison with LPP and traditional algorithms show that GLPP is promising.

This paper presents iris recognition for personal identification using neural networks. Iris recognition system consists of localization of the iris region and generation of data set of iris images and then iris pattern recognition. One of the problems in iris recognition is fast and accurate localization of the iris image. In this paper, fast algorithm is used for the localization of the inner and outer boundaries of the iris region. Located iris is extracted from an eye image, and, after normalization and enhancement it is represented by a data set. Using this data set a neural network is applied for the classification of iris patterns. Results of simulations illustrate the effectiveness of the neural system in personal identification.

Imaging algorithms often require reliable methods to evaluate the quality effects of the visual artifacts that digital processing brings about. This paper adopts a no-reference objective method for predicting the perceived quality of images in a deterministic fashion. Principal Component Analysis is first used to assemble a set of objective features that best characterize the information in image data. Then a neural network, based on the Circular Back-Propagation (CBP) model, associates the selected features with the corresponding predictions of quality ratings and reproduces the scores process of human assessors. The neural model allows one to decouple the process of feature selection from the task of mapping features into a quality score. Results on a public database for an image-quality experiment involving JPEG compressed-images and comparisons with existing objective methods confirm the approach effectiveness.

This paper presents a bio-inspired connectionist approach for motion description through sequences of images. First, this approach is based on the architecture of oriented columns and the strong local and distributed interactions of the neurons in the primary visual cortex (V1). Secondly, in the integration and combination of their responses in the middle temporal area (MT). I propose an architecture in two layers : a causal spatio-temporal filtering (CSTF) of Gabor-like type which captures the oriented contrast and a mechanism of antagonist inhibitions (MAI) which estimates the motion. The first layer estimates the local orientation and speed, the second layer classifies the motion (global response) and both describe the motion and the pursuit trajectory. This architecture has been evaluated on sequences of natural and synthetic images.

This work investigates the role of color in object recognition. We approach the problem from a computational perspective by measuring the performance of biologically inspired object recognition methods. As benchmarks, we use image datasets proceeding from a real-world object detection scenario and compare classification performance using color and gray-scale versions of the same datasets. In order to make our results as general as possible, we consider object classes with and without intrinsic color, partitioned into 4 datasets of increasing difficulty and complexity. For the same reason, we use two independent bio-inspired models of object classification which make use of color in different ways. We measure the qualitative dependency of classification performance on classifier type and dataset difficulty (and used color space) and compare to results on gray-scale images. Thus, we are able to draw conclusions about the role and the optimal use of color in classification and find that our results are in good agreement with recent psychophysical results.