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A novel method to measure the graph similarity is proposed, where the labels, in-degrees, and out-degrees of the vertices in the graph are comprehensively considered in order to conquer the high complexity and information loss in the measurement process. In this proposal, the graph is decomposed into a generalized tree and its similarity can be measured by computing the cost of the node sequence triples...
This paper addressed multivariate calibration based on least square support vector machines (LS-SVM) regression to provide a powerful model for machine learning and data mining. LS-SVM technique have the advantages to provide the capability of learning a high dimensional feature with fewer training data, and to decrease the computational complexity for requiring only solving a set of linear equation...
This paper presents an modification to the method of Bellman Residual Elimination (BRE) for approximate dynamic programming. While prior work on BRE has focused on learning an approximate policy for an underlying Markov Decision Process (MDP) when the state transition model of the MDP is known, this work proposes a model-free variant of BRE that does not require knowledge of the state transition model...
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