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We are organising a special session on Interactive Data Analysis and Visualization at the 2012 International Joint Conference on Neural Networks! The special session web site contains all the details.

By offering automated information extraction tools from data, machine learning has revolutionized the way in which humans can cope with electronic data volumes. The ever increasing complexity of the settings continues to pose challenges to the field: often, it is no longer possible to specify a priori a formal learning task; complex parameter choices can severely influence the outcome; and an appropriate encoding of data is not clear at all. More and more often, the human constitutes an important step in the loop to interactively decide about an appropriate learning model, model parameters, and data representation. Because of this fact, intuitive models and model parameters, and human understandable interfaces to the model and data are needed. In this frame, interesting new technologies have been developed such as high quality data visualization tools, sparse interpretable data representation and models, informed priors, active learning, and similar.

This special session aims to foster research in neural learning paradigms which offer an intuitive interface to data or models and thus have the potential as parts of an interactive pipeline. Go to the special session web site more information.

In our new paper (Relating Fisher information to order parameters. Physical Review E, 84(4):041116, 2011), we study phase transitions and relevant order parameters via statistical estimation theory using the Fisher information matrix. The assumptions that we make limit our analysis to order parameters representable as a negative derivative of thermodynamic potential over some thermodynamic variable. Nevertheless, the resulting representation is sufficiently general and explicitly relates elements of the Fisher information matrix to the rate of change in the corresponding order parameters. The obtained relationships allow us to identify, in particular, second-order phase transitions via divergences of individual elements of the Fisher information matrix. A computational study of random Boolean networks (RBNs) supports the derived relationships, illustrating that Fisher information of the magnetization bias (that is, activity level) is peaked in finite-size networks at the critical points, and the maxima increase with the network size. The framework presented here reveals the basic thermodynamic reasons behind similar empirical observations reported previously. The study highlights the generality of Fisher information as a measure that can be applied to a broad range of systems, particularly those where the determination of order parameters is cumbersome.