Catálogo de publicaciones - libros

Self-organizing maps (SOM) have been recognized as a powerful tool in data exploratoration, especially for the tasks of clustering on high dimensional data. However, clustering on categorical data is still a challenge for SOM. This paper aims to extend standard SOM to handle feature values of categorical type. A batch SOM algorithm (NCSOM) is presented concerning the dissimilarity measure and update method of map evolution for both numeric and categorical features simultaneously.

Most of the approaches are memory-like and run outside of the database. On the other hand, when we deal with data warehouse the size of tables is extremely huge for memory copy. In addition, using a pure SQL-like approach is quite inefficient. Actually, those implementations rarely take advantages of database programming. Furthermore, RDBMS vendors offer a lot of features for taking control and management of the data. We purpose a approach by means of for finding . The main idea is to avoid from the database, saving both the copying and process context switching, , and . The empirical evaluation of our approach shows that runs competitively with the most known implementations based on SQL. Our performance evaluation was made with SQL Server 2000 (v.8) and T-SQL, throughout several synthetical datasets.

In this work we propose to use a more powerful teacher to effectively apply query learning algorithms to identify regular languages in practical, real-world problems. More specifically, we define a more powerful set of replies to the membership queries posed by the L* algorithm that reduces the number of such queries by several orders of magnitude in a practical application. The basic idea is to avoid the needless repetition of membership queries in cases where the reply will be negative as long as a particular condition is met by the string in the membership query. We present an example of the application of this method to a real problem, that of inferring a grammar for the structure of technical articles.

In this paper, we propose a framework named UMT (User-profile Modeling based on Transactional data) for modeling user group profiles based on the transactional data. UMT is a generic framework for application systems that keep the historical transactions of their users. In UMT, user group profiles consist of three types: basic information attributes, synthetic attributes and probability distribution attributes. User profiles are constructed by clustering user transaction data and integrating cluster attributes with domain information extracted from application systems and other external data sources. The characteristic of UMT makes it suitable for personalization of transaction-based commercial application systems. A case study is presented to illustrate how to use UMT to create a personalized tourism system capable of using domain information in intelligent ways and of reacting to external events.

Latent Segments Models (LSM) are commonly used as an approach for market segmentation. When using LSM, several criteria are available to determine the number of segments. However, it is not established which criteria are more adequate when dealing with a specific application. Since most market segmentation problems involve the simultaneous use of categorical and continuous base variables, it is particularly useful to select the criteria when dealing with LSM with mixed type base variables. We first present an empirical test, which provides the ranking of several information criteria for model selection based on ten mixed data sets. As a result, the ICL-BIC, BIC, CAIC and criteria are selected as the performing criteria in the estimation of mixed mixture models. We then present an application concerning a retail chain clients’ segmentation. The information criteria yield two segments: and .

A new machine learning approach is presented for automatic detection of Mediterranean water eddies from sea surface temperature maps of the Atlantic Ocean. A pre-processing step uses Laws’ convolution kernels to reveal microstructural patterns of water temperature. Given a map point, a numerical vector containing information on local structural properties is generated. This vector is forwarded to a multi-layer perceptron classifier that is trained to recognise texture patterns generated by positive and negative instances of eddy structures. The proposed system achieves high recognition accuracy with fast and robust learning results over a range of different combinations of statistical measures of texture properties. Detection results are characterised by a very low rate of false positives. The latter is particularly important since meddies occupy only a small portion of SST map area.

We propose a methodology to monitor the quality of the meta-data used to describe content in web portals. It is based on the analysis of the meta-data using statistics, visualization and data mining tools. The methodology enables the site’s editor to detect and correct problems in the description of contents, thus improving the quality of the web portal and the satisfaction of its users. We also define a general architecture for a platform to support the proposed methodology. We have implemented this platform and tested it on a Portuguese portal for management executives. The results validate the methodology proposed.

Space applications’ users have been relying for the past decades on custom developed software tools capable of addressing short term necessities during critical Spacecraft control periods. Advances in computing power and storage solutions have made possible the development of innovative decision support systems. These systems are capable of providing high quality integrated data to both near real time and historical data analysis applications. This paper describes the implementation of a new approach for a distributed and loosely coupled data extraction and transformation solution capable of extracting, transforming and perform loading of relevant real-time and historical Space Weather and Spacecraft data from semi-structured text files into an integrated space-domain decision support system. The described solution takes advantage of XML and Web Service technologies and is currently working under operational environment at the European Space Agency as part of the Space Environment Information System for Mission Control Purposes (SEIS) project.

Research in robotics has traditionally emphasized low-level sensing and control tasks, path planning and actuator design and control. In contrast, generally using robotic simulators, several Artificial Intelligence (AI) researchers are more concerned with providing real/simulated robots with higher-level cognitive functions that enable them to reason, act and perceive in an autonomous way in dynamic, inaccessible, continuous and non deterministic environments. Combining results from traditional robotics with those from AI and cognitive science will be thus essential for the future of intelligent robotics.

The purpose of the 1st International Workshop on Intelligent Robotics IROBOT’ 05 was to bring together researchers, engineers and other professionals interested in the application of Artificial Intelligence techniques in real/simulated robotics to discuss current work and future directions.

This paper presents a novel approach to analyze the appearance of human motions with a simple model i.e. mapping the motions using a virtual marionette model. The approach is based on a robot using a monocular camera to recognize the person interacting with the robot and start tracking its head and hands. We reconstruct 3-D trajectories from 2-D image space (IS) by calibrating and fusing the camera images with data from an inertial sensor, applying general anthropometric data and restricting the motions to lie on a plane. Through a virtual marionette model we map 3-D trajectories to a feature vector in the . This implies inversely that now a certain set of 3-D motions can be performed by the (virtual) marionette system. A subset of these motions are considered to convey information (i.e. gestures). Thus, we are aiming to build up a database which keeps the vocabulary of gestures represented as signals in the . The main contribution of this work is the computational model of the . We introduce the guide robot “Nicole” to place our system in an embodied context. We sketch two novel approaches to represent human motion (i.e. Marionette Space and Labananalysis). We define a gesture vocabulary organized in three sets (i.e. Cohen’s Gesture Lexicon, Pointing Gestures and Other Gestures).