Colloquium

Boosting is a general method for producing a very accurate classification rule by combining rough and moderately inaccurate "rules of thumb". While rooted in a theoretical framework of machine learning, boosting has been found to perform quite well empirically.

Science values simplicity. All other things being equal, a simple explanation is preferable to a complex one. Bayesians assign higher prior probability to simple theories. But is it really true that a simple theory is a-priori more likely than a complex one?

WWW workload generators are used to evaluate web server performance, and thus have a large impact on what performance optimizations are applied to servers.

Peer-to-peer (p2p), initially conceived for the purpose of sharing music in the Internet, promises to be a more general paradigm for organizing large-scale distributed applications.

Search is one of the most ubiquitous and important applications used on the internet, but it is also one of the hardest applications to do well.

In past decades, the theory of computational complexity has flourished in terms of both the revelation of its internal structures and the unfolding of its numerous applications. In this paper we discuss several persistent and interwoven themes underlying many of these accomplishments.

The behavior of the real market is quite complicated and the analysis is very difficult. The computer simulation of the market provide us with powerful tools to understand the essential elements for a certain phenomenon of the market like bubbles.