Recurrent neural networks serve as black-box models for nonlinear dynamical systems identification and time series prediction. Training of recurrent networks typically minimizes t...
Predicting the throughput of large TCP transfers is important for a broad class of applications. This paper focuses on the design, empirical evaluation, and analysis of TCP throug...
We present PROUD - A PRObabilistic approach to processing similarity queries over Uncertain Data streams, where the data streams here are mainly time series streams. In contrast t...
Mi-Yen Yeh, Kun-Lung Wu, Philip S. Yu, Ming-Syan C...
Progressive loss of the field of vision is characteristic of a number of eye diseases such as glaucoma, a leading cause of irreversible blindness in the world. Recently, there has ...
The Hurst parameter H characterizes the degree of long-range dependence (and asymptotic selfsimilarity) in stationary time series. Many methods have been developed for the estimat...
Stilian Stoev, Murad S. Taqqu, Cheolwoo Park, Geor...