We present a new online learning algorithm in the selective sampling framework, where labels must be actively queried before they are revealed. We prove bounds on the regret of ou...
Recent years have seen rapid growth of online services that rely on large-scale server clusters to handle high volume of requests. Such clusters must adaptively control the CPU ut...
This paper1 presents an efficient modeling technique for data streams in a dynamic spatiotemporal environment and its suitability for mining developing trends. The streaming data a...
Previous object code compression schemes have employed static and semiadaptive compression algorithms to reduce the size of instruction memory in embedded systems. The suggestion ...
Background: Testing for selection is becoming one of the most important steps in the analysis of multilocus population genetics data sets. Existing applications are difficult to u...
Tiago Antao, Ana Lopes, Ricardo J. Lopes, Albano B...