Learning Rules from User Behaviour

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A New Approach Adapting Neural Network Classifiers to Sudden Changes in Nonstationary Environments

Business are increasingly analyzing streaming data in real time to achieve business objectives such as monetization or quality control. The predictive algorithms applied to streaming data sources are often trained sequentially by updating the model weights after each new data point arrives. When disruptions or changes in the data generating process occur, the online learning process allows the algorithm to slowly learn the changes; however, there may be a period of time after concept drift during which the predictive algorithm underperforms. This thesis introduces a method that makes online neural network classifiers more resilient to these concept drifts by utilizing data about concept drift to update neural network parameters

Algoritmos eficientes, incrementales y escalables para el aprendizaje en redes de neuronas artificiales

[Abstract] The core of this work is the development of new supervised learning methods for feedforward neural networks. In the first place, improvements on already developed learning methods are presented in order to generalize their behaviour to new situations while keeping their original characteristics. In particular, the employment of the regularization technique is proposed to handle situations where the overfitting to data is possible. Secondly, we develop online learning algorithms for one and two layer neural networks so as to allow their application in stationary and non stationary contexts in which the process to be modelled does not remain unalterable. In addition, for the case of two layers neural networks, this algorithm will also automatically adapt the topology of the network according to the needs of learning, by adding new hidden units only when necessary. In all cases, an optimum employment of the computational resources is pursued. For all the proposed algorithms, theoretical descriptions of their capacities are included, and their behaviours are illustrated by means of their application to significant cases. Finally, we analyze the results obtained, extracting the main conclusions about each method, their capacities and limitations for their future application. [Resumo] Este traballo céntrase no desenvolvemento de novos modelos de aprendizaxe supervisada para redes de neuronas artificiais alimentadas cara a adiante. En primeiro lugar, expóñense melloras sobre métodos de aprendizaxe xa desenvolvidos, co obxecto de xeneralizar o seu comportamento a novas situacións mantendo as súas características orixinais. En concreto, móstrase o emprego da regularización para manexar situacións onde é posible o fenómeno de sobreaxuste aos datos. En segundo lugar, desenvólvense algoritmos de aprendizaxe online tanto para redes dunha capa como de dúas, que permiten a súa aplicación en contornas non estacionarias en que o proceso que se vai modelar non permanece inalterable. Alén diso, para o caso das redes de dúas capas, este algoritmo online permitirá que tamén a topoloxía da rede se adapte de xeito automático segundo as necesidades da aprendizaxe, engadindo unidades ocultas unicamente en caso de que sexa necesario. En todo momento perséguese un aproveitamento dos recursos dispoñibles. Para os algoritmos propostos, inclúese unha descrición teórica das súas capacidades e o seu comportamento ilústrase mediante a súa aplicación a casos concretos e significativos. Finalmente, analízanse os resultados obtidos, extraendo as principais conclusións do comportamento de cada método e as súas capacidades e limitacións para a súa aplicación futura. [Resumen] Este trabajo se centra en el desarrollo de nuevos modelos de aprendizaje supervisado para redes de neuronas artificiales alimentadas hacia delante. En primer lugar, se plantean mejoras sobre métodos de aprendizaje ya desarrollados, con el objeto de generalizar su comportamiento a nuevas situaciones manteniendo sus características originales. En concreto, se plantea el empleo de la regularización para manejar situaciones donde es posible el fenómeno de sobreajuste a los datos. En segundo lugar, se desarrollan algoritmos de aprendizaje online tanto para redes de una capa como de dos, que permiten su aplicación en entornos no estacionarios en los que el proceso a modelar no permanece inalterable. Además, para el caso de las redes de dos capas, este algoritmo online permitirá que también la topología de la red se adapte de manera autom ática según las necesidades del aprendizaje, añadiendo unidades ocultas únicamente en caso necesario. En todo momento se persigue un aprovechamiento de los recursos disponibles. Para los algoritmos propuestos se incluye una descripción teórica de sus capacidades, y su comportamiento se ilustra mediante su aplicación a casos concretos y significativos. Finalmente se analizan los resultados obtenidos, extrayendo las principales conclusiones del comportamiento de cada método, capacidades y limitaciones para su aplicación futura

Relational reinforcement learning for planning with exogenous effects

Probabilistic planners have improved recently to the point that they can solve difficult tasks with complex and expressive models. In contrast, learners cannot tackle yet the expressive models that planners do, which forces complex models to be mostly handcrafted. We propose a new learning approach that can learn relational probabilistic models with both action effects and exogenous effects. The proposed learning approach combines a multi-valued variant of inductive logic programming for the generation of candidate models, with an optimization method to select the best set of planning operators to model a problem. We also show how to combine this learner with reinforcement learning algorithms to solve complete problems. Finally, experimental validation is provided that shows improvements over previous work in both simulation and a robotic task. The robotic task involves a dynamic scenario with several agents where a manipulator robot has to clear the tableware on a table. We show that the exogenous effects learned by our approach allowed the robot to clear the table in a more efficient way.Peer ReviewedPostprint (published version

Incremental learning and concept drift in INTHELEX

Real-world tasks often involve a continuous flow of new information that affects the learned theory, a situation that classical batch (one-step) learning systems are hardly suitable to handle. On the contrary, incremental (also called “on-line”)techniques are able to deal with such a situation by exploiting refinement operators. In many cases deep knowledge about the world is not available: Either incomplete information is available at the time of initial theory generation, or the nature of the concepts evolves dynamically. The latter situation is the most difficult to handle since time evolution needs to be considered. This work presents a new approach to learning in presence of concept drift, and in particular a special version of the incremental system INTHELEX purposely designed to implement such a technique. Its behavior in this context has been checked and analyzed by running it on two different datasets