Titlebook: Neural Information Processing; 20th International C Minho Lee,Akira Hirose,Rhee Man Kil Conference proceedings 2013 Springer-Verlag Berlin

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0302-9743 imaging; vision, speech and signal processing; control, robotics and hardware technologies and novel approaches and applications.978-3-642-42041-2978-3-642-42042-9Series ISSN 0302-9743 Series E-ISSN 1611-3349

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cardiac-arrest

Obstacle

Brand-Centric Recommendation with Inter-brand Similarities,ntric recommendation, brands have few social links to other brands, so trust between brands is difficult to use. In this paper we present a method for brand-centric recommendation where . are implicitly determined by decomposing a brand-follower matrix. This social information on inter-brand similar

wangle

One-Side Probability Machine: Learning Imbalanced Classifiers Locally and Globally,Our proposed model has also established close connections with various famous models such as BMPM and Support Vector Machine. One appealing feature is that the optimization problem involved can be cast as a convex second order conic programming problem with a global optimum guaranteed. A series of e

endoscopy

An Improved ISOMAP for Visualization and Classification of Multiple Manifolds,single manifold. But if the data are sampled from multi-class, each of which corresponds to an independent manifold, and clusters formed by data points belonging to each class are separated away, several disconnected neighborhood graphs will occur, which leads to the failure of ISOMAP. Moreover, ISO

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Probability of Perfect Reconstruction of Pareto Set in Multi-Objective Optimization,he statistical learning theory and completely reconstructs the Pareto set with high probability from a finite number of single-objective optimizations. We also derived an upper bound of the mean of the probability that the method fails the perfect reconstruction. Our analysis shows the mean error pr

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A Median Variant of Generalized Learning Vector Quantization,ning, for which only dissimilarity information between the objects to be classified is available. For this purpose, the cost function of GLVQ is reformulated as a probabilistic model such that a generalized expectation maximization scheme can be applied as learning procedure. We give a rigorous math

ADORN

Mingle

Contextual Bandits for Context-Based Information Retrieval,only on the few top-ranked documents results. To obtain feedbacks on other documents, the system has to explore the non-top-ranked documents that could lead to a better solution. However, the system also needs to ensure that the quality of result lists is high by exploiting what is already known. Cl