William H. Hsu

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We present an alternative to standard genetic programming (GP) that applies layered learning techniques to decompose a problem. GP is applied to subproblems sequentially, where the population in the last generation of a subproblem is used as the initial population of the next subproblem. This method is applied to evolve agents to play keepaway soccer, a(More)
We describe a method to extract content text from diverse Web pages by using the HTML document's text-to-tag ratio rather than specific HTML cues that may not be constant across various Web pages. We describe how to compute the text-to-tag ratio on a line-by-line basis and then cluster the results into content and non-content areas. With this approach we(More)
We consider the problems of predicting, classifying, and annotating friends relations in friends networks, based upon network structure and user profile data. First, we document a data model for the blog service LiveJournal, and define a set of machine learning problems such as predicting existing links and estimating inter-pair distance. Next, we explain(More)
One ant is placed initially at each of the given terminal vertices that are to be connected. In each iteration, an ant is moved to a new location via an edge, determined stochastically, but biased in such a manner that the ants get drawn to the paths traced out by one another. Each ant maintains its own separate list of vertices already visited to avoid(More)
Identification of nodes relevant to a given node in a relational network is a basic problem in network analysis with great practical importance. Most existing network analysis algorithms utilize one single relation to define relevancy among nodes. However, in real world applications multiple relationships exist between nodes in a network. Therefore, network(More)
In this paper, we address the problem of link recommendation in weblogs and similar social networks. First, we present an approach based on collaborative recommendation using the link structure of a social network and content-based recommendation using mutual declared interests. Next, we describe the application of this approach to a small representative(More)
We present an adaptation of the standard genetic program (GP) to hierarchically decomposable, multi-agent learning problems. To break down a problem that requires cooperation of multiple agents, we use the team objective function to derive a simpler, intermediate objective function for pairs of cooperating agents. We apply GP to optimize first for the(More)