Objective: Knowledge acquisition and maintenance in medical domains with a large application domain ontology is a difficult task. To reduce knowledge elicitation costs, semiautoma...
In this position paper, we first review the state-of-the-art in graph-based semi-supervised learning, and point out three limitations that are particularly relevant to multimedia ...
In this paper we present a method to cluster large datasets that change over time using incremental learning techniques. The approach is based on the dynamic representation of clus...
Input feature ranking and selection represent a necessary preprocessing stage in classification, especially when one is required to manage large quantities of data. We introduce a ...
We prove the strongest known bound for the risk of hypotheses selected from the ensemble generated by running a learning algorithm incrementally on the training data. Our result i...