Data Cleaning publication categorizer

A method is described for the minimization of a function of n variables, which depends on the comparison of function values at the (n + 1) vertices of a general simplex, followed by the replacement of the vertex with the highest value by another point. The simplex adapts itself to the local landscape, and contracts on to the final minimum. The method is shown to be effective and computationally compact. A procedure is given for the estimation of the Hessian matrix in the neighbourhood of the minimum, needed in statistical estimation problems.

In the feature subset selection problem, a learning algorithm is faced with the problem of selecting a relevant subset of features upon which to focus its attention, while ignoring the rest. To achieve the best possible performance with a particular learning algorithm on a particular training set, a feature subset selection method should consider how the algorithm and the training set interact. We explore the relation between optimal feature subset selection and relevance. Our wrapper method searches for an optimal feature subset tailored to a particular algorithm and a domain.

Structured and semi-structured object representations are getting more
and more important for modern database applications. Examples for such data are
hierarchical structures including chemical compounds, XML data or image data.
As a key feature, database systems have to support the search for similar objects
where it is important to take into account both the structure and the content features
of the objects. A successful approach is to use the edit distance for tree
structured data. As the computation of this measure is NP-complete, constrained