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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-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)
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)
The rise of hyperlinked networks has made a wealth of data readily available. However, the Web's browsing paradigm does not strongly support retrieving and integrating data from multiple sites. Today, the only way to integrate the huge amount of available data is to build specialized applications, which are time-consuming, costly to build, and difficult to(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-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)
ChAln Ta:nrln rragAIearl d+y I " l. " UI) l " ll .l " I UJ1ax) rx1'111 Gvv U. Abstract The Web is based on a browsing paradigm that makes it difficult to retrieve and integrate data from multiple sites. y-k&y, the only w&y t;g & this is to bl_?ild_ specialized applications, which are time-consuming to develop and difficult to maintain. We are addressing(More)