DAVID P. HELMBOLD
Professor, Computer Science

Phone: 831-459-2016
Fax: 831-459-4829
dph@cse.ucsc.edu

Computer Science Department
E2 Building room 345B
University of California
Santa Cruz, CA 95064



David Helmbold's current research centers around machine learning and computational learning theory. In addition, to theoretical work, he has applied learning algorithms to practical problems such as determining when to spin down a disk drive in a portable computer to save power and detecting small objects in images.

Recent Publications

Damian Eads, Edward Rosten, and David Helmbold. Learning object location predictors with boosting and grammer-guided feature extraction in Proceedings of the British Machine Vision Conference (BMVC), September 2009.

David Helmbold and Aleatha Parker-Wood. All-moves-as-first heuristics in monte-carlo go in Hamid R. Arabnia, David de la Fuente, and Jose A. Olivas, editors, Proceedings of the 2009 International Conference on Artificial Intelligence, pages 605--610. WorldComp, July 2009.

David Helmbold and Manfred K. Warmuth. Learning Permutations with exponential weights in Journal of Machine Learning Research, vol. 10, pages 1687--1718, July 2009. (The conference version appeared in COLT 2007.)

S. Lodha, D. Fitzpatrick, and D. P. Helmbold. Aerial lidar data classification using adaboost in Proceedings of the 3D Digital Imaging and Modeling Conference (3DIM), pages 435--443, August 2007.

S. Lodha, D. Fitzpatrick, and D.P. Helmbold. Aerial lidar data classification using expectation-maximization In Proceedings of SPIE Conference on Vision Geometry XIV , volume 6499, January 2007.

O. Wang, S. Lodha, and D.P. Helmbold. A bayesian approach to building footprint extraction from aerial lidar data in Proceedings of the Third International Symposium on 3D Data Processing, Visualization and Transmission (3DPVT). IEEE, 2006.

Selected Publications

Here is a list containing many of my publications. It includes work in the areas of Object Detection and Terrain classification, Computer Go, Boosting, On-line learning, Other learning theory, and Learning Applications, as well as some older work on Debugging of Parallel Programs and Parallel Algorithms.

dph@cse.ucsc.edu,

Last modified October 25, 2009 (09:06:43 PM).