Javier Yáñez

• Complutense University of Madrid

The Continuous Hopfield Network (CHN) became one of the major breakthroughs in the come back of Neural Networks in the mid 80s, as it could be used to solve combinatorial optimization problems such as the Traveling Salesman Problem. Once researchers provided a mechanism, not based in trial-and-error, to guarantee the feasibility of the CHN, the qua...

We are totally immersed in the Big Data era and reliable algorithms and methods for data classification are instrumental for astronomical research. Random Forest and Support Vector Machines algorithms have become popular over the last few years and they are widely used for different stellar classification problems. In this article, we explore an al...

We are totally immersed in the Big Data era and reliable algorithms and methods for data classification are instrumental for astronomical research. Random Forest and Support Vector Machines algorithms have become popular over the last few years and they are widely used for different stellar classification problems. In this article, we explore an al...

The continuous Hopfield network (CHN) can be used to solve, among other combinatorial optimization problems, the traveling salesman problem (TSP). In order to improve the performance of this heuristic technique, a divide-and-conquer strategy based on two phases is proposed. The first phase involves linking cities with the most neighbors to define a...

The Continuous Hopfield Neural Network (CHN) is a neural network which can be used to solve some optimization problems. The weights of the network are selected based upon a set of parameters which are deduced by mapping the optimization problem to its associated CHN. When the optimization problem is the Traveling Salesman Problem, for instance, thi...

The Continuous Hopfield Neural Network (CHN) is a neural network which can be used to solve some optimization problems. The weights of the network are selected based upon a set of parameters which are deduced by mapping the optimization problem to its associated CHN. When the optimization problem is the Traveling Salesman Problem, for instance, thi...

In this paper we discuss some main image processing techniques in order to propose a classification based upon the output these methods provide. Because despite a particular image analysis technique can be supervised or unsupervised, and can allow or not the existence of fuzzy information at some stage, each technique has been usually designed to f...

In this work, we identify 63 bona fide new candidates to T Tauri stars (TTSs) in the Taurus-Auriga region using as baseline its ultraviolet excess. The initial data set has been defined from the GALEX all sky survey (AIS). The GALEX satellite obtained images in the near ultraviolet (NUV) and far ultraviolet (FUV) bands where the TTSs show a promine...

A crisp image segmentation can be characterized in terms of the set of edges that separates the adjacent regions of the segmentation. Based on these edges, an alternative way to define a fuzzy image segmentation is introduced in this paper. In this sense, the notion of fuzzy image segmentation is characterized by means of a fuzzy set over the set o...

In this paper we analyze the structural properties of the inconsistencies detected by the crude algorithm for segmentation of digital images introduced by some of the authors in a previous work. Such analysis will suggest an alternative algorithm for image segmentation.

In this paper we address the problem of inconsistency in preference relations, pointing out the relevance of a meaningful representation in order to help decision maker to capture such inconsistencies. Dimension theory framework, despite its computational complexity, is considered here, pursuing in principle a decomposition of arbitrary preference...

In this paper we develop a segmentation scheme for digital images based upon an iterative binary coloring technique that takes into account changing behavior of adjacent pixels. The output is a hierarchical structure of images which allows a better understanding of complex images. In particular, we propose two algorithms that should be considered a...

One of the main problems in practice is the difficulty in dealing with membership functions. Many decision makers ask for a graphical representation to help them to visualize results. In this paper, we point out that some useful tools for fuzzy classification can be derived from fuzzy coloring procedures. In particular, we bring here a crisp grey c...

Measuring criteria weights in multicriteria decision making is a key issue in order to amalgamate information when reality is being described from sev-eral different points of view. In this paper we propose a method for evaluating those weights taking advantage of Dimension Theory, which allows the repre-sentation of the set of alternatives within...

Given a graph G=(V,E), a coloring function C assigns an integer value C(i) to each node i∈V in such a way that the extremes of any edge {i,j}∈E cannot share the same color, i.e., C(i)≠C(j). Two different approaches to the graph coloring problem of a fuzzy graph are introduced in this paper. The classical concept of the (crisp) chromatic number of a...

The robust coloring problem (RCP) is an NP-Hard generalization of the minimal coloring problem and was introduced in [1,3]. Therefore, heuristic algorithms are needed to find workable and acceptable solutions to this problem. The RCP occur in timetabling problems, cluster analysis, coloring geographical maps, design and operation of flexible manufa...

Land cover analysis by means of remotely sensing images quite often suggests the existence of fuzzy classes, where no clear borders or particular shapes appear. In this paper we present an image classification aid algorithm which shows as its main output a processed image where each pixel is being colored according to the degree of similitude to th...

In this paper we present a pixel coloring algorithm, to be considered as a tool in fuzzy classification. Such an algorithm is based upon a sequential application of a divisive binary procedure on a fuzzy graph associated to the image to be classified, taking into account surrounding pixels. Each color will suggest a possible class, if homogeneous,...

Classical dimension theory, when applied to preference modeling, is based upon the assumption that linear ordering is the only elemental notion for rationality. In fact, crisp preferences are in some way decomposed into basic criteria, each one being a linear order. In this paper, we propose that indeed dimension is relative to a previous idea of r...

The more information a preference structure gives, the more sophisticated representation techniques are necessary, so decision makers can have a global view of data and therefore a comprehensive understanding of the problem they are faced with. In this paper we propose to explore valued preference relations by means of a search for the number of un...

Some problems can be modeled as graph coloring ones for which the criterion of minimizing the number of used colors is replaced by another criterion maintaining the number of colors as a constraint. Some examples of these problem types are introduced; it would be the case, for instance, of the problem of scheduling the courses at a university with...

Decision making based upon valued preference relations is assuming that each decision maker is able to consistently manage intensity values for preferences, but this is indeed a difficult task, even when dealing with few alternatives. Representation tools will therefore play a key role in order to help decision makers to understand their preference...

Translation of Classical Dimension Theory into a valued context should allow a comprehensible view of alternatives, by means of an informative representation, being this representation still manageable by decision makers. In fact, there is an absolute need for this kind of representations, since being able to comprehend a valued preference relation...