Le vie dell\u2019antico sono infinite? Appunti sulle fonti archeologiche negli elementi decorativi di Santa Maria presso San Satiro a Milano

A partire dal ricchissimo corpus di disegni dall\u2019Antico di et\ue0 umanistica e da una serie di vestigia antiche, il saggio individua i possibili modelli antiquari introdotti nei dettagli decorativi della chiesa milanese di Santa Maria Presso San Satiro e nei pregevoli capitelli figurati della Sacrestia, progettata da Bramante. Da questa analisi affiorano importanti (ed inedite) relazioni tra Milano, Urbino, Roma e Padova, utili a valutare la circolazione delle fonti antiche tra la fine del XV e l\u2019inizio del XVI secolo

Redes Neuronales Artificiales. Un Enfoque Práctico : P. Isasi Viñuela, I. M. Galván. Pearson Educación, 2004. ISBN 84-205-4025-0

Artificial Neural Networks belong to the sub-symbolic branch of Artificial Intelligence since they allow to find the solution of a problem without the need of knowing the algorithm necessary to solve it. This turns them into a tool based on an approach completely different from that used by conventional Computing. Artificial neural networks (ANN) have been inspired in how the brain works and in the way its cells relate to each other. Technological advances provide ever-greater resources to represent really complex structures, perform computations at high speed and in parallel. This has indeed motivated research on this kind of tool.Facultad de Informátic

Search engine ranking factors analysis : Moz digital marketing company survey study

Project Work presented as the partial requirement for obtaining a Master's degree in Information Management, specialization in Information Systems and Technologies ManagementThe use of the Internet increases every year in the world for multiple purposes and at significant rates. In the same way, access to electronic business and personal pages allowing commercial transactions follows these high evolution rates. Many studies on this subject have pointed that it is important for most businesses to have a web presence. The key to be found by the right product or service target audience, at the right moment, according to most of authors, lies with search engines (SE) advent. However, there had been frequently changes in search engines ranking website classification algorithms during the last years. To accomplish this model evolution, the Search Engine Optimization (SEO) professionals must to frequently adopt to constant changes regarding ranking classification strategies from SE schemas of work. In this work the author explored a wide range of factors that may influence search engine result pages (SERP’s) and examined recent aspects of user experience over a website that are increasing importance regarding the optimization to be done over the web pages, internal and external page links, and its technical components. In addition, it seems that the user action and involvement over the website are key factors that Google will probably continue to adopt to determine websites rank in SERP’s. As an empirical study, all efforts to discover the SE website promotion ranking factors are based on trial and error activities and there is no official knowledge base regarding these protected secrets kept by the major players of this valuable market. Due to the lack of published academic research works in this area, the present work has discovered and documented SE ranking factors based on survey data by a large quantity of companies in digital marketing segment. At the end of the project the author intends to present the state-of-the-art in this field of study as well as some market perception evolution of this subject based heavily on practical experiments and most recent literature in this area. Moreover, it is growing the debate about the limits of digital marketing. Due the powerful influence of SE to market and people behavior, the presented study data and considerations raise an important forum of discussion now and in the future concerning ethics and socially acceptable limits and controls over personal information on the internet

Classification rules obtained from dynamic self-organizing maps

La obtención de conocimiento a partir de la información existente es un proceso no trivial que consiste en identificar patrones válidos, novedosos, potencialmente útiles y comprensibles a partir de los datos disponibles. La Minería de Datos es el área de la Informática referida a la aplicación de diferentes métodos para la obtención de patrones y modelos. Una de las soluciones más utilizadas se basa en estrategias adaptativas no supervisadas que permitan clasificar la información disponible. En esta dirección, las redes neuronales competitivas dinámicas han demostrado ser capaces de brindar buenos resultados. Sin embargo, su aplicación en el área de la Minería de Datos se encuentra limitada por su funcionamiento tipo “caja negra” donde resulta complejo justificar el conocimiento adquirido. Este artículo propone una nueva estrategia para obtener reglas de clasificación a partir de una red neuronal competitiva dinámica entrenada con el método AVGSOM. Dicho método ha sido seleccionado en base a su capacidad para preservar la topología de los datos de entrada, característica fundamental para obtener los hipercubos iniciales adecuados. La estrategia desarrollada en este trabajo combina la capacidad del aprendizaje no supervisado del AVGSOM con información disponible del problema para reducir la dimensión del antecedente de las reglas. El método propuesto ha sido aplicado a tres conjuntos de datos obtenidos del repositorio UCI con resultados muy satisfactorios. En particular, los resultados obtenidos en la clasificación de la base de datos Iris han sido comparados con otros métodos existentes mostrando la superioridad del nuevo método propuesto. Finalmente se presentan algunas conclusiones así como algunas líneas de trabajo futuras.Knowledge discovery from existing information is a non-trivial process that consists in identifying valid, new, potentially useful, and understandable patterns from available data. Data Mining is the area of Computer Sciences which refers to the application of different methods so as to obtain patterns and models. One of the mostly used solutions is based on non-supervised adaptive strategies allowing the classification of available data. Towards this direction, dynamic competitive neural networks have proved to be capable of providing good results. However, their application in the area of Data Mining is constrained due to their “black box” type functioning, in which it is hard to justify the acquired knowledge. This paper proposes a new strategy for obtaining classification rules from a dynamic competitive neural network trained with the AVGSOM method. Such method has been selected for its capacity of preserving input data topology, essential characteristic necessary to obtain the proper initial hypercubes. The strategy developed in this paper combines non-supervised learning of AVGSOM and the information available of the problem in order to reduce the dimension of rule antecedent. The proposed method has been applied to three sets of data obtained from UCI repository with really satisfactory results. In particular, the results obtained in the Iris data base classification have been compared with other existing methods showing the supremacy of the new proposed method. Finally, some of the conclusions as well as some future lines of work are presented.VII Workshop de Agentes y Sistemas Inteligentes (WASI)Red de Universidades con Carreras en Informática (RedUNCI

Classification rules obtained from dynamic self-organizing maps

La obtención de conocimiento a partir de la información existente es un proceso no trivial que consiste en identificar patrones válidos, novedosos, potencialmente útiles y comprensibles a partir de los datos disponibles. La Minería de Datos es el área de la Informática referida a la aplicación de diferentes métodos para la obtención de patrones y modelos. Una de las soluciones más utilizadas se basa en estrategias adaptativas no supervisadas que permitan clasificar la información disponible. En esta dirección, las redes neuronales competitivas dinámicas han demostrado ser capaces de brindar buenos resultados. Sin embargo, su aplicación en el área de la Minería de Datos se encuentra limitada por su funcionamiento tipo “caja negra” donde resulta complejo justificar el conocimiento adquirido. Este artículo propone una nueva estrategia para obtener reglas de clasificación a partir de una red neuronal competitiva dinámica entrenada con el método AVGSOM. Dicho método ha sido seleccionado en base a su capacidad para preservar la topología de los datos de entrada, característica fundamental para obtener los hipercubos iniciales adecuados. La estrategia desarrollada en este trabajo combina la capacidad del aprendizaje no supervisado del AVGSOM con información disponible del problema para reducir la dimensión del antecedente de las reglas. El método propuesto ha sido aplicado a tres conjuntos de datos obtenidos del repositorio UCI con resultados muy satisfactorios. En particular, los resultados obtenidos en la clasificación de la base de datos Iris han sido comparados con otros métodos existentes mostrando la superioridad del nuevo método propuesto. Finalmente se presentan algunas conclusiones así como algunas líneas de trabajo futuras.Knowledge discovery from existing information is a non-trivial process that consists in identifying valid, new, potentially useful, and understandable patterns from available data. Data Mining is the area of Computer Sciences which refers to the application of different methods so as to obtain patterns and models. One of the mostly used solutions is based on non-supervised adaptive strategies allowing the classification of available data. Towards this direction, dynamic competitive neural networks have proved to be capable of providing good results. However, their application in the area of Data Mining is constrained due to their “black box” type functioning, in which it is hard to justify the acquired knowledge. This paper proposes a new strategy for obtaining classification rules from a dynamic competitive neural network trained with the AVGSOM method. Such method has been selected for its capacity of preserving input data topology, essential characteristic necessary to obtain the proper initial hypercubes. The strategy developed in this paper combines non-supervised learning of AVGSOM and the information available of the problem in order to reduce the dimension of rule antecedent. The proposed method has been applied to three sets of data obtained from UCI repository with really satisfactory results. In particular, the results obtained in the Iris data base classification have been compared with other existing methods showing the supremacy of the new proposed method. Finally, some of the conclusions as well as some future lines of work are presented.VII Workshop de Agentes y Sistemas Inteligentes (WASI)Red de Universidades con Carreras en Informática (RedUNCI

Keyword Identification in Spanish Documents using Neural Networks

The large amount of textual information digitally available today gives rise to the need for effective means of indexing, searching and retrieving this information. Keywords are used to describe briefly and precisely the contents of a textual document. In this paper we present an algorithm for keyword extraction from documents written in Spanish.This algorithm combines autoencoders, which are adequate for highly unbalanced classification problems, with the discriminative power of conventional binary classifiers. In order to improve its performance on larger and more diverse datasets, our algorithm trains several models of each kind through bagging.XII Workshop Bases de Datos y Minería de Datos (WBDDM)Red de Universidades con Carreras en Informática (RedUNCI

Keyword identification in Spanish documents using neural networks

The large amount of textual information digitally available today gives rise to the need for effective means of indexing, searching and retrieving this information. Keywords are used to describe briefly and precisely the contents of a textual document. In this paper we present an algorithm for keyword extraction from documents written in Spanish.This algorithm combines autoencoders, which are adequate for highly unbalanced classification problems, with the discriminative power of conventional binary classifiers. In order to improve its performance on larger and more diverse datasets, our algorithm trains several models of each kind through bagging.Facultad de Informátic

Cyclic evolution : A new strategy for improving controllers obtained by layered evolution

Complex control tasks may be solved by dividing them into a more specific and more easily handled subtasks hierarchy. Several authors have demonstrated that the incremental layered evolution paradigm allows obtaining controllers capable of solving this type of tasks. In this direction, different solutions combining Incremental Evolution with Evolving Neural Networks have been developed in order to provide an adaptive mechanism minimizing the previous knowledge necessary to obtain a good performance giving place to controllers made up of several networks. This paper is focused on the presentation of a new mechanism, called Cyclic Evolution, which allows improving controllers based on neural networks obtained through layered evolution. Its performance is based on continuing the cyclic improvement of each of the networks making up the controller within the whole domain of the problem. The proposed method of this paper has been used to solve the Keepaway game with successful results compared to other solutions recently proposed. Finally, some conclusions are included together with some future lines of workVI Workshop de Agentes y Sistemas Inteligentes (WASI)Red de Universidades con Carreras en Informática (RedUNCI

Keyword Identification in Spanish Documents using Neural Networks

The large amount of textual information digitally available today gives rise to the need for effective means of indexing, searching and retrieving this information. Keywords are used to describe briefly and precisely the contents of a textual document. In this paper we present an algorithm for keyword extraction from documents written in Spanish.This algorithm combines autoencoders, which are adequate for highly unbalanced classification problems, with the discriminative power of conventional binary classifiers. In order to improve its performance on larger and more diverse datasets, our algorithm trains several models of each kind through bagging.XII Workshop Bases de Datos y Minería de Datos (WBDDM)Red de Universidades con Carreras en Informática (RedUNCI