Way of scoring by Spanish First Division volleyball teams in relation to winning/losing, home/away, final classification, and type of confrontation

The aim of this study was to assess the way volleyball teams score with regard to: whether or not they won the game, whether they were the home or away team, the level of the opposing teams, and the type of confrontation. The sample was composed of 118,083 plays from 794 men’s volleyball matches and 125,751 plays from 719 women’s matches of Spain’s first division clubs (from the 2002-2003 season to the 2006-2007 season). The variables studied were: the way points were obtained in each play, being the home or away team, the level of the teams, the result of the match, and the type of confrontation between the teams with regard to their level. The results demonstrate that for both men’s and women’s teams, the majority of the points were obtained in attack and by opponent errors. Differences were found with regard to the way points were obtained when winning or losing the match was taken into account as well as when considering the level of the teams. This paper discusses the differences found with regard to whether the team is home or visiting and the type of confrontation.

TEXT-MESS: Intelligent, Interactive and Multilingual Text Mining based on Human Language Technologies, TIN2006-15265-C06

The goal of the project is to analyze, experiment, and develop intelligent, interactive and multilingual Text Mining technologies, as a key element of the next generation of search engines, systems with the capacity to find "the need behind the query". This new generation will provide specialized services and interfaces according to the search domain and type of information needed. Moreover, it will integrate textual search (websites) and multimedia search (images, audio, video), it will be able to find and organize information, rather than generating ranked lists of websites.

Morphological classification of microtidal sand and gravel beaches

A new classification of microtidal sand and gravel beaches with very different morphologies is presented below. In 557 studied transects, 14 variables were used. Among the variables to be emphasized is the depth of the Posidonia oceanica. The classification was performed for 9 types of beaches: Type 1: Sand and gravel beaches, Type 2: Sand and gravel separated beaches, Type 3: Gravel and sand beaches, Type 4: Gravel and sand separated beaches, Type 5: Pure gravel beaches, Type 6: Open sand beaches, Type 7: Supported sand beaches, Type 8: Bisupported sand beaches and Type 9: Enclosed beaches. For the classification, several tools were used: discriminant analysis, neural networks and Support Vector Machines (SVM), the results were then compared. As there is no theory for deciding which is the most convenient neural network architecture to deal with a particular data set, an experimental study was performed with different numbers of neuron in the hidden layer. Finally, an architecture with 30 neurons was chosen. Different kernels were employed for SVM (Linear, Polynomial, Radial basis function and Sigmoid). The results obtained for the discriminant analysis were not as good as those obtained for the other two methods (ANN and SVM) which showed similar success.