1,721,135 research outputs found
Statistical Machine Learning Group, Canberra
No full textThis report is the background theory for Discrete Component Analysis software called DCA. Cur-rently the software is run in stand-alone mode, and scavengers data streaming libraries and Dirichlet utilities from the older MPCA system1. The software itself is written in the C language and compiles on a Linux and a Mac OS X environment. The models presented here are a hier-archical extension of discrete component analysis. This is known under many names [2], such a
Theory Refinement on Bayesian Networks
No full textTheory refinement is the task of updating a domain theory in the light of new cases, to be done automatically or with some expert assistance. The problem of theory refinement under uncertainty is reviewed here in the context of Bayesian statistics, a theory of belief revision. The problem is reduced to an incremental learning task as follows: the learning system is initially primed with a partial theory supplied by a domain expert, and thereafter maintains its own internal representation of alternative theories which is able to be interrogated by the domain expert and able to be incrementally refined from data. Algorithms for refinement of Bayesian networks are presented to illustrate what is meant by "partial theory", "alternative theory representation ", etc. The algorithms are an incremental variant of batch learning algorithms from the literature so can work well in batch and incremental mode. 1 Introduction Theory refinement is the task of updating a domain theory in the light of..
Chain Graphs for Learning
No full textChain graphs combine directed and undirected graphs and their underlying mathematics combines properties of the two. This paper gives a simplified definition of chain graphs based on a hierarchical combination of Bayesian (directed) and Markov (undirected) networks. Examples of a chain graph are multivariate feed-forward networks, clustering with conditional interaction between variables, and forms of Bayes classifiers. Chain graphs are then extended using the notation of plates so that samples and data analysis problems can be represented in a graphical model as well. Implications for learning are discussed in the conclusion. 1 Introduction Probabilistic networks are a notational device that allow one to abstract forms of probabilistic reasoning without getting lost in the mathematical detail of the underlying equations. They offer a framework whereby many forms of probabilistic reasoning can be combined and performed on probabilistic models without careful hand programming. Efforts ..
Standards for Open Source Information Retrieval
No full textStandards are important because they make a field more open to small and medium businesses and to academic players. We review a number of standards that apply to information retrieval and web search, and discuss the role that they play. We also discuss some areas where there is potential for the development of standards, where for instance information retrieval would benefit, and where standards development appears feasible
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