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In a multi-view problem, the features of the domain can be partitioned into disjoint subsets (views) that are sufficient to learn the target concept. Semi-supervised, multi-view algorithms, which reduce the amount of labeled data required for learning, rely on the assumptions that the views are compatible and uncorrelated (i.e., every example is identically(More)
With the tremendous amount of information that becomes available on the Web on a daily basis, the ability t o q u i c kly develop information agents has become a crucial problem. A vital component o f a n y W eb-based information agent is a set of wrappers that can extract the relevant data from semistructured information sources. Our novel approach to(More)
INTRODUCTION Inductive learning algorithms typically use a set of labeled examples to learn class descriptions for a set of user-specified concepts of interest. In practice, labeling the training examples is a tedious, time consuming, error-prone process. Furthermore, in some applications , the labeling of each example also may be extremely expensive (e.g.,(More)
Multi-view learners reduce the need for labeled data by exploiting disjoint subsets of features (views), each of which is sufficient for learning. Such algorithms assume that each view is a strong view (i.e., perfect learning is possible in each view). We extend the multi-view framework by introducing a novel algorithm, Aggressive Co-Testing, that exploits(More)
Multi-view algorithms reduce the amount of required training data by partitioning the domain features into separate subsets or views that are sufficient to learn the target concept. Such algorithms rely on the assumption that the views are sufficiently compatible for multi-view learning (i.e., most examples are labeled identically in all views). In(More)
A critical problem in developing information agents for the Web is accessing data that is formatted for human use. We have developed a set of tools for extracting data from web sites and transforming it into a structured data format, such as XML. The resulting data can then be used to build new applications without having to deal with unstructured data. The(More)
Multi-agent collaboration or teamwork and learning are two critical research c hal-lenges in a large number of multi-agent applications. These research c hallenges are highlighted in RoboCup, an international project focused on robotic and synthetic soccer as a common testbed for research i n m ulti-agent systems. This article describes our approach to(More)
In this paper we provide a fast, data-driven solution to the <i>failing query</i> problem: given a query that returns an empty answer, how can one relax the query's constraints so that it returns a non-empty set of tuples? We introduce a novel algorithm, <sc>loqr</sc>, which is designed to relax queries that are in the disjunctive normal form and contain a(More)