Catálogo de publicaciones - libros

In this paper, we propose an evolutionary approach to deal with shortcomings on conventional focused crawling systems in semantic web environment. Thereby, meta-evolution strategy for collaboration among multiple crawlers has to be efficiently carried out. It is based on incremental aggregation of partial semantic structures extracted from web resources, which are in advance annotated with local ontologies. To do this, we employ similarity-based matching algorithm, so that fitness function is formulated by summing all possible semantic similarities. As a result, the best mapping condition (i.e., the fitness is maximized) is obtained for efficiently ) reconciling semantic conflicts between multiple crawlers, and ) evolving semantic structures of web spaces over time.

This paper introduces the use of 15 different readability indices as a fingerprint that enables the classification of documents into different categories. While a classification based on such fingerprints alone is not necessarily superior to document categorization based on dedicated dictionaries per se, the document fingerprints can enhance the overall classification rate by applying proper data fusion techniques. For other applications text mining related applications such as language classification, the detection of plagiarism, or author identification, the accuracy of text categorization methods based on readability fingerprints can even exceed a dictionary-based approach. A novel addition to the readability indices is the addition of histograms based on the word length of all the dictionary words used in the text and a dictionary of the most common easy words in the English language.

The immense amount of unstructured information available on the Web poses increasing difficulties to fulfill users’ needs. New tools are needed to automatically collect and filter information that meets users’ demands. This paper presents the architecture of a personal information agent that mines web sources and retrieves documents according to users’ interests. The agent operates in two modes: "generation of space of concepts" and "document filtering". A space of concepts for a domain is represented by a matrix of asymmetrical coefficients of similarity for each pair of relevant terms in the domain. This matrix is seen as a Hopfield neural network, used for document filtering, where terms represent neurons and the coefficients of similarity the weights of the links that connect the neurons. Experiments conducted to evaluate the approach show that it exhibits satisfactory effectiveness.

Recurrent Neural Network (RNN) models have been shown to perform well on artificial grammars for sequential classification tasks over long-term time-dependencies. However, there is a distinct lack of the application of RNNs to real-world text classification tasks. This paper presents results on the capabilities of extended two-context layer SRN models (xRNN) applied to the classification of the Reuters-21578 corpus. The results show that the introduction of high levels of noise to sequences of words in titles, where noise is defined as the unimportant stopwords found in natural language text, is very robustly handled by the classifiers which maintain consistent levels of performance. Comparisons are made with SRN and MLP models, as well as other existing classifiers for the text classification task.

Multidimensional Scaling algorithms (MDS) are useful tools that help to discover high dimensional object relationships. They have been applied to a wide range of practical problems and particularly to the visualization of the semantic relations among documents or terms in textual databases.

The MDS algorithms proposed in the literature often suffer from a low discriminant power due to its unsupervised nature and to the ‘curse of dimensionality’. Fortunately, textual databases provide frequently a manually created classification for a subset of documents that may help to overcome this problem.

In this paper we propose a semi-supervised version of the Torgerson MDS algorithm that takes advantage of this document classification to improve the discriminant power of the word maps generated. The algorithm has been applied to the visualization of term relationships. The experimental results show that the model proposed outperforms well known unsupervised alternatives.

Forecasting Internet traffic is receiving an increasing attention from the computer networks domain. Indeed, by improving this task efficient traffic engineering and anomaly detection tools can be developed, leading to economic gains due to better resource management. This paper presents a Neural Network (NN) approach to predict TCP/IP traffic for all links of a backbone network, using both univariate and multivariate strategies. The former uses only past values of the forecasted link, while the latter is based on the neighbor links of the backbone topology. Several experiments were held by considering real-world data from the UK education and research network. Also, different time scales (e.g. every ten minutes and hourly) were analyzed. Overall, the proposed NN approach outperformed other forecasting methods (e.g. Holt-Winters).

An ANN-based classifier for voltage wave disturbance was developed. Voltage signals captured on the power transmission system of CHESF, Federal Power Utility, were processed in two steps: by wavelet transform and principal component analysis. The classification was carried out using a combination of six MLPs with different architectures: five representing the first to fifth-level details, and one representing the fifth-level approximation. Network combination was formed using the boosting algorithm which weights a model’s contribution by its performance rather than giving equal weight to all models. Experimental results with real data indicate that boosting is clearly an effective way to improve disturbance classification accuracy when compared with the simple average and the individual models.

This paper presents an approach (SOINN-DTW) for recognition of temporal data that is based on Self-Organizing Incremental Neural Network (SOINN) and Dynamic Time Warping. Using SOINN’s function that eliminates noise in the input data and represents topological structure of input data, SOINN-DTW method approximates output distribution of each state and is able to construct robust model for temporal data. SOINN-DTW method is the novel method that enhanced Stochastic Dynamic Time Warping Method (Nakagawa,1986). To confirm the effectiveness of SOINN-DTW method, we present an extensive set of experiments that show how our method outperforms HMM and Stochastic Dynamic Time Warping Method in classifying phone data and gesture data.

Quantitative models are very successful forr extrapolating the basic trend-cycle component of time series. On the contrary time series models failed to handle adequately shocks or irregular events, that is non-periodic events such as oil crises, promotions, strikes, announcements, legislation etc. Forecasters usually prefer to use their own judgment in such problems. However their efficiency in such tasks is in doubt too and as a result the need of decision support tools in this procedure seem to be quite important. Forecasting with neural networks has been very popular across the Academia in the last decade. Estimating the impact of irregular events has been one of the most successful application areas. This study examines the relative performance of Artificial Neural Networks versus Multiple Linear Regression for estimating the impact of expected irregular future events.

Biomedical signals are generally contaminated with artifacts and noise. In case artifacts dominate, the useful signal can easily be extracted with projective subspace techniques. Then, biomedical signals which often represent one dimensional time series, need to be transformed to multi-dimensional signal vectors for the latter techniques to be applicable. In this work we propose the application of a greedy kernel Principal Component Analysis(KPCA) which allows to decompose the multidimensional vectors into components, and we will show that the one related with the largest eigenvalues correspond to an high-amplitude artifact that can be subtracted from the original.