—A variety of clustering algorithms have recently been proposed to handle data that is not linearly separable; spectral clustering and kernel k-means are two of the main methods....
ended abstract reports on on-going work exploring the addition of a certain degree of control over expressivity in a unit selection context. Rather than merely choosing one unit se...
We present the design, correctness, and analysis of SONDe, a simple fully decentralized object deployment algorithm for highly requested systems. Given an object (service or data)...
Vincent Gramoli, Anne-Marie Kermarrec, Erwan Le Me...
In this paper we investigate the regularization property of Kernel Principal Component Analysis (KPCA), by studying its application as a preprocessing step to supervised learning ...
The efficiency of most pitch estimation methods declines when the analyzed frame is shortened and/or when a wide fundamental frequency (F0) range is targeted. The technique propo...