Clustering, or factoring of a document collection attempts to “explain” each ob- served document in terms of one or a small number of inferred prototypes. Prior work demonstrated that when links exist between documents in the corpus (as is the case with a collection of web pages or scientific papers), building a joint model of document contents and connections produces a better model than that built from contents or connections alone. Many problems arise when trying to apply these joint models to corpus at the scale of the World Wide Web, however; one of these is that the sheer overhead of representing a feature space on the order of billions of dimensions becomes impractical. We address this problem with a simple representational shift inspired by proba- bilistic relational models: instead of representing document linkage in terms of the identities of linking documents, we represent it by the explicit and inferred at- tributes of the linking documents. Several surprising results come with this shift: in addition to being computationally more tractable, the new model produces fac- tors that more cleanly decompose the document collection. We discuss several variations on this model and show how some can be seen as exact generalizations of the PageRank algorithm.

A good software architecture facilitates application system development, promotes achievement of functional requirements, and supports system reconfiguration. We present a domain-specific software architecture (DSSA) that we have developed for a large application domain of adaptive intelligent systems (AISs). The DSSA provides: (a) an AIS reference architecture designed to meet the functional requirements shared by applications in this domain, (b) principles for decomposing expertise into highly reusable components, and (c) an application configuration method for selecting relevant components from a library and automatically configuring instances of those components in an instance of the architecture. The AIS reference architecture incorporates features of layered, pipe and filter, and blackboard style architectures. We describe three studies demonstrating the utility of our architecture in the subdomain of mobile office robots and identify software engineering principles embodied in the architecture.>

We present MLC++, a library of C++ classes and tools for supervised machine learning. While MLC++ provides general learning algorithms that can be used by end users, the main objective is to provide researchers and experts with a wide variety of tools that can accelerate algorithm development, increase software reliability, provide comparison tools, and display information visually. More than just a collection of existing algorithms, MLC++ is can attempt to extract commonalities of algorithms and decompose them for a unified view that is simple, coherent, and extensible. In this paper we discuss the problems MLC++ aims to solve, the design of MLC++, and the current functionality.>