We present a unified framework for reasoning about worst-case regret bounds for learning algorithms. This framework is based on the theory of duality of convex functions. It brin...
In probabilistic grammatical inference, a usual goal is to infer a good approximation of an unknown distribution P called a stochastic language. The estimate of P stands in some cl...
Variational Bayesian Expectation-Maximization (VBEM), an approximate inference method for probabilistic models based on factorizing over latent variables and model parameters, has ...
— In this paper, we study how the cooperative relaying can improve both capacity and fairness in cellular network. The capacity and fairness have a trade-off relationship, so inc...
Seungho Song, Kyuho Son, Hyang-Won Lee, Song Chong
Abstract. In this paper, we introduce a novel evolution-based segmentation algorithm by using the heat flow analogy, to gain practical advantage. The proposed algorithm consists of...