Catálogo de publicaciones - libros

Defeasible reasoning is a rule-based approach for efficient reasoning with incomplete and conflicting information. Nevertheless, it is based on solid mathematical formulations and is not fully comprehensible by end users, who often need graphical trace and explanation mechanisms for the derived conclusions. Directed graphs (or digraphs) can assist in this affair, but their applicability is balanced by the fact that it is difficult to associate data of a variety of types with the nodes and the connections in the graph. In this paper we try to utilize digraphs in the graphical representation of defeasible rules, by exploiting their expressiveness, but also trying to counter their major disadvantage, by defining multiple node and connection types.

We present a novel technique for efficient Content Based Peer to Peer Image Retrieval (CBP2PIR) that employs a Content Addressable Network (CAN). A two-stage color histogram based method is described. The first stage defines mapping into the CAN by use of a single fuzzy histogram; while the second stage completes the image retrieval process through a spatially-biased histogram. The proposed system is completely decentralized, non-flooding, and promises high image recall, while minimizing network traffic.

This paper explores the effectiveness of machine learning techniques in detecting firms that issue fraudulent financial statements (FFS) and deals with the identification of factors associated to FFS. To this end, a number of experiments have been conducted using representative learning algorithms, which were trained using a data set of 164 fraud and non-fraud Greek firms in the recent period 2001-2002. This study indicates that a decision tree can be successfully used in the identification of FFS and underline the importance of financial ratios.

The objective of this study is to investigate the discrimination of benign from malignant breast lesions using: the linear, the feedforward neural network, the k-nearest neighbor and the boosting classifiers. Nuclear morphometric parameters from cytological smears taken by Fine Needle Aspiration (FNA) of the breast, have been measured from patients. These parameters undergo an appropriate transformation and then, the classifiers are performed on the raw and on the transformed data. The results show that in terms of the raw data set all classifiers exhibit almost the same performance (overall accuracy  ≡ 87%), Thus the linear classifier suffices for the discrimination of the present problem. Also, based on the previous results, one can conjecture that the use of these classifiers combined with image morphometry and statistical techniques for feature transformation, may offer useful information towards the improvement of the diagnostic accuracy of breast FNA.

This paper proposes and compares several data fusion techniques for robot navigation. The fusion techniques investigated here are several topologies of the Kalman filter. The problem that had been simulated is the navigation of a robot carrying two sensors, one Global Positioning System (GPS) and one Inertial Navigation System (INS). For each of the above topologies, the statistic error and its, mean value, variance and standard deviation were examined.

This paper describes two simple techniques that can greatly improve navigation and motion control of nonholonomic robots based on range sensor data. The first technique enhances sensory information by re-using recent sensor data through coordinate transformation, whereas the second compensates for errors due to long control cycle times by forward projection through the kinematic model of the robot. Both techniques have been succesfully tested on a Nomad 200 mobile robot.

Let (,) denote an undirected graph, and being the sets of its nodes and edges, respectively. A in (,) is a subset of edges with no common endpoints. Finding a matching of maximum cardinality constitutes the maximum cardinality matching (MCM) problem. For a thorough theoretical discussion we refer to [6]. The MCM problem is of specific interest from a Constraint Programming (CP) point of view because it can model several logical constraints (predicates) like the and the predicates [7]. Thus, the definition of a maximum cardinality matching constraint provides a framework encompassing other predicates. Along this line of research, we define a global constraint with respect to the MCM and address the issue of consistency. Establishing hyper-arc consistency implies the identification of edges that cannot participate in any maximum cardinality matching. Evidently, this issue (also called ) is related to the methods developed for solving the problem. Solving this problem for bipartite graphs was common knowledge long before Edmonds proposed an algorithm for the non-bipartite case [3]. Regarding hyper-arc consistency, the problem has been resolved only for the bipartite case [1].

In this paper, we analyze the potential of using Emergent Self-Organizing Maps (ESOMs) based on Kohonen Self –Organizing maps in order to detect intrusive behaviours. The proposed approach combines machine learning and information visualization techniques to analyze network traffic and is based on classifying “normal” versus “abnormal” traffic. The results are promising as they show the ability of eSOMs to classify normal against abnormal behaviour regarding false alarms and detection probabilities.

This paper presents a conceptual mapping framework between a formal and visual process modelling language, Fundamental Business Process Modelling Language (FBPML), and the Web Services Ontology (OWL-S), aiming to bridge the gap between Enterprise Modelling methods and Semantic Web services. The framework is divided into a data model and a process model component. An implementation and an evaluation of the process model mapping are demonstrated.

Color plays an important role in web site design. The selection of effective chromatic combinations and the relation of color to the perceived aesthetic and emotional value of a web site is the focus of this paper. The subject of the reported research has been to define a model through which to be able to associate color combinations with specific desirable emotional and aesthetic values. The presented approach involves application of machine learning techniques on a rich data set collected during a number of empirical studies.