Estimating Dependency Structure as a Hidden Variable

This paper introduces a probability model, the mixture of trees that can account for sparse, dynamically changing dependence relationships. We present a family of efficient algorithms that use EMand the Minimum Spanning Tree algorithm to find the ML and MAP mixtureof trees for a variety of priors, including the Dirichlet and the MDL priors.

Estimating Dependency Structure as a Hidden Variable

This paper introduces a probability model, the mixture of trees that can account for sparse, dynamically changing dependence relationships. We present a family of efficient algorithms that use EM and the Minimum Spanning Tree algorithm to find the ML and MAP mixture of trees for a variety of priors, including the Dirichlet and the MDL priors. We also show that the single tree classifier acts like an implicit feature selector, thus making the classification performance insensitive to irrelevant attributes. Experimental results demonstrate the excellent performance of the new model both in density estimation and in classification.

Mobile P2Ping: A Super-Peer based Structured P2P System Using a Fleet of City Buses

Recently, researchers have introduced the notion of super-peers to improve signaling efficiency as well as lookup performance of peer-to-peer (P2P) systems. In a separate development, recent works on applications of mobile ad hoc networks (MANET) have seen several proposals on utilizing mobile fleets such as city buses to deploy a mobile backbone infrastructure for communication and Internet access in a metropolitan environment. This paper further explores the possibility of deploying P2P applications such as content sharing and distributed computing, over this mobile backbone infrastructure. Specifically, we study how city buses may be deployed as a mobile system of super-peers. We discuss the main motivations behind our proposal, and outline in detail the design of a super-peer based structured P2P system using a fleet of city buses.