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This paper develops a new Bayesian method for cluster detection, the ?Bayesian spatial scan statistic,? and compares this method to the standard (frequentist) scan statistic approach on the task of prospective disease surveillance.

We propose a new Bayesian method for spatial cluster detection, the ?Bayesian spatial scan statistic,? and compare this method to the standard (frequentist) scan statistic approach. We demonstrate that the Bayesian statistic has several advantages over the frequentist approach, including increa...

A K-means tutorial. We present new algorithms for the k-means clustering problem. They use the kd-tree data structure to reduce the large number of nearest-neighbor queries issued by the traditional algorithm. Sufficient statistics are stored in the nodes of the kd-tree. Then, an analysis of th...

This is an extended version of the KDD99 paper (available here. We present new algorithms for the k-means clustering problem. They use the kd-tree data structure to reduce the large number of nearest-neighbor queries issued by the traditional algorithm. Sufficient statistics are stored in the no...

Link data, consisting of a collection of subsets of entities, can be an important source of information for a variety of fields including the social sciences, biology, criminology, and business intelligence. However, these links may be incomplete, containing one or more unknown members. We consi...

Stability is a desirable characteristic for linear dynamical systems, but it is often ignored by algorithms that learn these systems from data. We propose a novel method for learning stable linear dynamical systems: we formulate an approximation of the problem as a convex program, start with a ...

To make efficient use of a dynamic system such as a mechanical manipulator, the robotic controller needs various models of its behaviour. I describe a method of learning in which all the experiences in the lifetime of the robot are explicitly remembered. They are stored in a manner which permits...

We introduce a novel active-learning scenario in which a user wants to work with a learning algorithm to identify {em useful} anomalies. These are distinguished from the traditional statistical definition of anomalies as outliers or merely ill-modeled points. Our distinction is that the usefulne...

We present an efficient algorithm to actively select queries for learning  the boundaries separating a function domain into regions where the function is above and below a given threshold.  We develop experiment selection methods based on entropy, misclassification rate, variance, and their com...

Traditional design of experiments (DOE) from the statistics literature focuses on optimizing an output parameter over a space of continuous input parameters. Here we consider DOE, or active learning, for descrete input spaces. A trivial example of this is the k-armed bandit problem, which is the...