Due to the diierent characteristics of replicated data objects and networks, highly adjustable replication schemes for the management of replicated data objects are required. Data replication schemes can be regarded as special instances of cooperation schemes, which describe the interaction of independent nodes within a computer network in order to achieve a common goal. This paper presents a n...

This paper describes a solution to the replica management problem in asynchronous distributed systems in which processes can crash and recover. Our solution is based on a Atomic Broadcast primitive which, in turn, is based on an underlying Consensus algorithm. The proposed technique makes a bridge between established results on Weighted Voting and recent results on the Consensus problem.

Classification methods have been widely used during last years in order to predict patterns and trends of interest in data. In present paper, a multiclassifier approach that combines the output of some of the most popular data mining algorithms is shown. The approach is based on voting criteria, by estimating the confidence distributions of each algorithm individually and combining them accordi...

Pattern recognition problems span a broad range of applications, where each application has its own tolerance on classification error. The varying levels of risk associated with many pattern recognition applications indicate the need for an algorithm with the ability to measure its own confidence. In this work, the supervised incremental learning algorithm Learn++ [1], which exploits the synerg...

We propose Compatibility Weighted Voting Games, a variant of Weighted Voting Games in which some pairs of agents are compatible and some are not. In a Weighted Voting Game each agent has a weight, and a set of agents can form a winning coalition if the sum of their weights is at least a given quota. Whereas the original Weighted Voting Game model assumes that all agents are compatible, we consi...

An incremental learning algorithm, Learn—, is introduced, for learning additional information from new data, even when new data include examples of previously unseen classes. Learn++ takes advantage of synergistic generalization performance of an ensemble of simple classifiers, each trained with a strategically chosen subset of the training database. As new data become available, new classifier...