Abstract. Adversarial bandit problems studied by Auer et al. [4] are multi-armed bandit problems in which no stochastic assumption is made on the nature of the process generating the rewards for actions. In this paper, we extend their theories to the case where k(≥ 1) distinct actions are selected at each time step. As algorithms to solve our problem, we analyze an extension of Exp3
We introduce a stricter Web community definition to overcome boundary ambiguity of a Web community defined by Flake, Lawrence and Giles [2], and consider the problem of finding communities that satisfy our definition. We discuss how to find such communities and hardness of this problem.We also propose Web page partitioning by equivalence relation defined using the class of communities of our definition. Though the problem of efficiently finding all communities of our definition is NP-complete, we propose an efficient method of finding a subclass of communities among the sets partitioned by each of n − 1 cuts represented by a GomoryHu tree [10], and partitioning a Web graph by equivalence relation defined using the subclass.According to our preliminary experiments, partitioning by our method divided the pages retrieved by keyword search into several different categories to some extent.
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