Learning dynamic Bayesian network structures provides a principled mechanism for identifying conditional dependencies in time-series data. An important assumption of traditional D...
Abstract. We propose a model for communication in single-hop wireless sensor networks and define and evaluate the performance of a robust, energy balanced protocol for a powerful a...
In this paper we present the growing hierarchical self-organizing map. This dynamically growing neural network model evolves into a hierarchical structure according to the requirem...
There has been a recent, growing interest in classification and link prediction in structured domains. Methods such as conditional random fields and relational Markov networks sup...
Abstract— This paper explores integrated source-channel decoding, driven by wireless sensor network applications where correlated information acquired by the network is gathered ...