The master algorithm: how the quest for the ultimate learning machine will remake our world : Pedro Domingos. Basic Books. 2015. ISBN 978-0465065707

Nowadays, “machine learning” is present in several aspects of the current world, internet advisors, advertisements and “smart” devices that seem to know what we need in a given moment. These are some examples of the problems solved by machine learning. This book presents the past, the present and the future of the different types of machine learning algorithms. At the beginning of the book, the author takes us to the first years of the computing science, where a programmer had to do absolutely everything by himself to make an algorithm do a certain task. As time passes, there appeared the first algorithms that were capable of programming themselves learning from the available data. The author presents what he himself calls the five “tribes” of machine learning, the essence that defends each one and the kind of problems that are able to solve without problems. With a great amount of simple examples, the author depicts which advantages and disadvantages of the “master” algorithms of each “tribes” are, saying that the problem that a tribe solves perfectly well, another one cannot do it, and the other way about. The author suggests to get the best out of each “tribe” and make a unique learning algorithm able to learn without caring about the problem: the master algorithm.Facultad de Informátic

Mapas auto-organizativos dinámicos

En este trabajo de grado es de interés resolver problemas de agrupamiento de información utilizando mapas auto-organizativos dinámicos. El objetivo es estudiar y analizar las estrategias considerando los mapas auto-organizativos tanto estáticos como dinámicos y proponer un nuevo método de entrenamiento para estas arquitecturas competitivas que permita mejorar la preservación de la topología de los datos de entrada. La eficacia del método propuesto será medida en base a su aplicación en la resolución de diversos problemas.Facultad de Informátic

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

The master algorithm: how the quest for the ultimate learning machine will remake our world : Pedro Domingos. Basic Books. 2015. ISBN 978-0465065707

Nowadays, “machine learning” is present in several aspects of the current world, internet advisors, advertisements and “smart” devices that seem to know what we need in a given moment. These are some examples of the problems solved by machine learning. This book presents the past, the present and the future of the different types of machine learning algorithms. At the beginning of the book, the author takes us to the first years of the computing science, where a programmer had to do absolutely everything by himself to make an algorithm do a certain task. As time passes, there appeared the first algorithms that were capable of programming themselves learning from the available data. The author presents what he himself calls the five “tribes” of machine learning, the essence that defends each one and the kind of problems that are able to solve without problems. With a great amount of simple examples, the author depicts which advantages and disadvantages of the “master” algorithms of each “tribes” are, saying that the problem that a tribe solves perfectly well, another one cannot do it, and the other way about. The author suggests to get the best out of each “tribe” and make a unique learning algorithm able to learn without caring about the problem: the master algorithm.Facultad de Informátic

Mapas auto-organizativos dinámicos

En este trabajo de grado es de interés resolver problemas de agrupamiento de información utilizando mapas auto-organizativos dinámicos. El objetivo es estudiar y analizar las estrategias considerando los mapas auto-organizativos tanto estáticos como dinámicos y proponer un nuevo método de entrenamiento para estas arquitecturas competitivas que permita mejorar la preservación de la topología de los datos de entrada. La eficacia del método propuesto será medida en base a su aplicación en la resolución de diversos problemas.Facultad de Informátic

Knowledge extraction in large databases using adaptive strategies

The general objective of this thesis is the development of an adaptive technique for extracting knowledge in large databases. Nowadays, technology allows storing huge volumes of information. For this reason, the availability of techniques that allow, as a first stage, analyzing that information and obtaining knowledge that can be expressed as classification rules, is of interest. However, the information available is expected to change and/or increase with time, and therefore, as a second stage, it would be relevant to adapt the knowledge acquired to the changes or variations affecting the original data set. The contribution of this thesis is focused on the definition of an adaptive technique that allows extracting knowledge from large databases using a dynamic model that can adapt to information changes, thus obtaining a data mining technique that can generate useful knowledge and produce results that the end user can exploit. The results of this research work can be applied to areas such as soil analysis, genetic analysis, biology, robotics, economy, medicine, plant failure detection, and mobile systems communications. In these cases, obtaining an optimal result is important, since this helps improve the quality of the decisions made after the process.Es revisión de: http://sedici.unlp.edu.ar/handle/10915/4215Resumen de la tesis presentada por el autor el día 27 de marzo de 2012 para la obtención del título de Doctor en Ciencias Informática (UNLP).Facultad de Informátic

lpPSO - New optimization strategy inspired by PSO

Given the large number of optimization problems that mankind faces, metaheuristics are very important strategies for the resolution of these problems. These strategies assess the suitability of the individuals, which represent solutions to the problem, a large number of times throughout the search for an optimal solution. When the assessment of an individual takes significant time or resources, the assessment of hundreds or thousands of individuals is a problem to be taken into consideration. In this paper, a strategy based on PSO that considerably reduces the number of individual assessments is presented, which is of great help for complex problems. The method proposed was compared with the classical version of PSO using classic functions in the space and a real case with a simulation model, and satisfactory results were obtained.Presentado en el XII Workshop 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

lpPSO - New optimization strategy inspired by PSO

Given the large number of optimization problems that mankind faces, metaheuristics are very important strategies for the resolution of these problems. These strategies assess the suitability of the individuals, which represent solutions to the problem, a large number of times throughout the search for an optimal solution. When the assessment of an individual takes significant time or resources, the assessment of hundreds or thousands of individuals is a problem to be taken into consideration. In this paper, a strategy based on PSO that considerably reduces the number of individual assessments is presented, which is of great help for complex problems. The method proposed was compared with the classical version of PSO using classic functions in the space and a real case with a simulation model, and satisfactory results were obtained.Presentado en el XII Workshop Agentes y Sistemas Inteligentes (WASI)Red de Universidades con Carreras en Informática (RedUNCI

Some stylized facts of the Bitcoin market

In recent years a new type of tradable assets appeared, generically known as cryptocurrencies. Among them, the most widespread is Bitcoin. Given its novelty, this paper investigates some statistical properties of the Bitcoin market. This study compares Bitcoin and standard currencies dynamics and focuses on the analysis of returns at different time scales. We test the presence of long memory in return time series from 2011 to 2017, using transaction data from one Bitcoin platform. We compute the Hurst exponent by means of the Detrended Fluctuation Analysis method, using a sliding window in order to measure long range dependence. We detect that Hurst exponents changes significantly during the first years of existence of Bitcoin, tending to stabilize in recent times. Additionally, multiscale analysis shows a similar behavior of the Hurst exponent, implying a self-similar process.Comment: 17 pages, 6 figures. arXiv admin note: text overlap with arXiv:1605.0670