We propose to use AdaBoost to efficiently learn classifiers over very large and possibly distributed data sets that cannot fit into main memory, as well as on-line learning wher...
Idle workstations in a network represent a significant computing potential. In particular, their processing power can be used by parallel-distributed programs that treat the netwo...
Michael J. Feeley, Brian N. Bershad, Jeffrey S. Ch...
The Web abounds with dyadic data that keeps increasing by every single second. Previous work has repeatedly shown the usefulness of extracting the interaction structure inside dya...
Advances in computing and networking technology, and an explosion in information sources has resulted in a growing number of distributed systems getting constructed out of resourc...
We present a scalable algorithm for the parallel computation of inverted files for large text collections. The algorithm takes into account an environment of a high bandwidth netw...
Berthier A. Ribeiro-Neto, Joao Paulo Kitajima, Gon...