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Alexander Gray

Document Type: Person
Tags: Auton Fast Classifiers, Statistical Data Mining for Astrophysics, K Nearest Neighbor, Astrostatistics, Cached Sufficient Statistics, Clustering, Memory-based Learning, Efficient Statistical Algorithms, Life Science Data Mining, Locally Weighted Learning, Kernel Density Estimation, Bayesian Networks, Kd-trees and Ball-trees, Mixture Models, Optimization

Alex's fascinations in early grade school were Legos, breaking ciphers, and drawing human anatomy. After studying Applied Math and Computer Science at Berkeley, he resisted a job offer to do Hollywood special effects and ended up working at NASA's Jet Propulsion Laboratory for six years developi...

Andrew Moore

Document Type: Person
Tags: Link Analysis, Auton Fast Classifiers, Statistical Data Mining for Astrophysics, Cached Sufficient Statistics, Efficient Statistical Algorithms, Spatial Statistics, Life Science Data Mining, Logistic Regression, Locally Weighted Learning, GDA, AD-trees, Bayesian Networks, Kernel Density Estimation, Kd-trees and Ball-trees, Mixture Models, WSARE, Reinforcement Learning, Active Learning, Markov Decision Processes, K Nearest Neighbor, Astrostatistics, Clustering, Memory-based Learning, Biosurveillance, Applications, Optimization, Association Rules

Andrew began his career writing video-games for an obscure British personal computer. He rapidly became a thousandaire and retired to academia, where he received a PhD from the University of Cambridge in 1991. He researched robot learning as a Post-doc working with Chris Atkeson, and then moved ...

An Empirical Investigation of Brute Force to choose Features, Smoothers and Function Approximators

Document Type: Paper
Tags: Memory-based Learning, Locally Weighted Learning

The generalization error of a function approximator, feature set or smoother can be estimated directly by the leave-one-out cross-validation error. For memory-based methods, this is computationally feasible. We describe an initial version of a general memory-based learning system (GMBL): a large...

Artur Dubrawski

Document Type: Person
Tags: GDA, Biosurveillance, Memory-based Learning, Mixture Models, Applications, Optimization, Association Rules, Locally Weighted Learning, Active Learning, Food Safety, Link Analysis, Social Networks, Dynamic Social Networks, Health of Equipment, Nuclear Safety

Artur Dubrawski considers himself a scientist and a practitioner. He has been tainted with real world entrepreneurial experiences. He had started up a successful company specializing in integration and deployment of advanced control systems and technological devices. He had also been affiliated ...

A tutorial on using the Vizier memory-based learning system

Document Type: Paper
Tags: Kd-trees and Ball-trees, Memory-based Learning, Efficient Statistical Algorithms, Locally Weighted Learning

A tutorial on using the Windows Vizier software, which provides a GUI for doing fast and autonomous locally weighted learning and nearest neighbor style learning. The Vizier software itself is available for free download at http://www.cs.cmu.edu/~awm/vizier, where you will find more details.

Brigham Anderson

Document Type: Person
Tags: AD-trees, Statistical Data Mining for Astrophysics, Astrostatistics, Efficient Statistical Algorithms, Memory-based Learning, Applications, Optimization, Association Rules, Reinforcement Learning, Locally Weighted Learning, Active Learning

Efficient Locally Weighted Polynomial Regression Predictions

Document Type: Paper
Tags: Kd-trees and Ball-trees, Memory-based Learning, Efficient Statistical Algorithms, Locally Weighted Learning

Locally weighted polynomial regression (LWPR) is a popular instance-based algorithm for learning continuous non-linear mappings. For more than two or three inputs and for more than a few thousand datapoints the computational expense of predictions is daunting. We discuss drawbacks with previous ...

Fast, Robust Adaptive Control by Learning only Forward Models

Document Type: Paper
Tags: Kd-trees and Ball-trees, Memory-based Learning, Efficient Statistical Algorithms, Active Learning, Locally Weighted Learning

A large class of motor control tasks requires that on each cycle the conctroller is told its current state and must choose an action to achieve a specified, state-dependent, goal behaviour. This paper argues that the optimization of learning rate, the number of experimental control decisions bef...

Hoeffding Races: Accelerating Model Selection Search for Classification and Function Approximation

Document Type: Paper
Tags: Memory-based Learning, Efficient Statistical Algorithms, Optimization, Active Learning, Locally Weighted Learning

Selecting a good model of a set of input points by cross validation is a computationally intensive process, especially if the number of possible models or the number of training points is high. Techniques such as gradient descent are helpful in searching through the space of models, but problems...

Interpolating Conditional Density Trees

Document Type: Paper
Tags: Statistical Data Mining for Astrophysics, Astrostatistics, Bayesian Networks, Efficient Statistical Algorithms, Kd-trees and Ball-trees, Mixture Models, Locally Weighted Learning

Joint distributions over many variables are frequently modeled by decomposing them into products of simpler, lower-dimensional conditional distributions, such as in sparsely connected Bayesian networks. However, automatically learning such models can be very computationally expensive in situatio...