Abstract. Knowledge Discovery in time series usually requires symbolic time series. Many discretization methods that convert numeric time series to symbolic time series ignore the ...
The hierarchical Dirichlet process hidden Markov model (HDP-HMM) is a flexible, nonparametric model which allows state spaces of unknown size to be learned from data. We demonstra...
Emily B. Fox, Erik B. Sudderth, Michael I. Jordan,...
It is common for simulation and analytical studies to model Internet traffic as an aggregation of mostly persistent TCP flows. In practice, however, flows follow a heavytailed ...
We present an approach for persistent tracking of moving objects observed by non-overlapping and moving cameras. Our approach robustly recovers the geometry of non-overlapping vie...
In light of advances in processor and networking technology, especially the emergence of network attached disks, the traditional client-server architecture becomes suboptimal for ...
Jiong Yang, Silvia Nittel, Wei Wang 0010, Richard ...