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Titlebook: Machine Learning and Data Mining in Pattern Recognition; 4th International Co Petra Perner,Atsushi Imiya Conference proceedings 2005 Spring

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Comparative Analysis of Genetic Algorithm, Simulated Annealing and Cutting Angle Method for Artifici We investigate two probabilistic global search method namely Genetic algorithm and Simulated annealing method and a deterministic cutting angle method to find weights in neural network. Experiments were carried out on UCI benchmark dataset.
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The Convex Subclass Method: Combinatorial Classifier Based on a Family of Convex Setsring sphere for a subset of positive samples and does not contain any negative samples. We also present a polynomial-time exact algorithm and an incremental randomized algorithm to compute it. In addition, we discuss the soft-classification version and evaluate these algorithms by some numerical experiments.
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Incremental Classification Rules Based on Association Rules Using Formal Concept Analysisithm requires just one database pass through the entire database. Individual classes can have different support threshold and pruning conditions such as criteria for noise and number of conditions in the classifier.
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Linear Manifold Clustering repeated sampling then continues recursively on each block of the partitioned data. A broad evaluation of some hundred experiments over real and synthetic data sets demonstrates the general superiority of this algorithm over any of the competing algorithms in terms of stability, accuracy, and computation time.
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Birds of a Feather Surf Together: Using Clustering Methods to Improve Navigation Prediction from Intopriate groups of navigation behaviour. The benefits of these methods over more established methods are highlighted. An empirical analysis is carried out on a sample of usage logs for Wireless Application Protocol (WAP) browsing as empirical support for the technique.
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