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Titlebook: Machine Learning and Data Mining in Pattern Recognition; 5th International Co Petra Perner Conference proceedings 2007 Springer-Verlag Berl

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Data Clustering: User’s Dilemmanumber of diverse disciplines. The goal is to partition a set of n d-dimensional points into k clusters, where k may or may not be known. Most clustering techniques require the definition of a similarity measure between patterns, which is not easy to specify in the absence of any prior knowledge abo
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On Concentration of Discrete Distributions with Applications to Supervised Learning of Classifiers inconclusive results about their impact. Some theoretical understanding of when they work is available, but a definite answer seems to be lacking. This paper derives distributions that maximizes the statewise difference to the respective product of marginals. These distributions are, in a sense the
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Comparison of a Novel Combined ECOC Strategy with Different Multiclass Algorithms Together with Parare is much need for improvements in the field of multiclass learning. We developed a novel combination algorithm called ., which is based on posterior class probabilities. It assigns, according to the Bayesian rule, the respective instance to the class with the highest posterior probability. A probl
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Multi-source Data Modelling: Integrating Related Data to Improve Model Performanceess the lack of model performance for data from which it is difficult to extract relationships. This paper proposes a set of algorithms that allow the integration of data from multiple datasets that are related, as well as results from the implementation of these techniques using data from the field
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An Empirical Comparison of Ideal and Empirical ROC-Based Reject Rulesst for a wrong classification can be so high that can be convenient to avoid a decision and reject the sample. This paper presents a comparison between two different reject rules (the Chow’s and the ROC rule). In particular, the experiments show that the Chow’s rule is inappropriate when the estimat
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