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» A Bayesian Metric for Evaluating Machine Learning Algorithms
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92
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JMLR
2006
80views more  JMLR 2006»
15 years 20 days ago
Using Machine Learning to Guide Architecture Simulation
An essential step in designing a new computer architecture is the careful examination of different design options. It is critical that computer architects have efficient means by ...
Greg Hamerly, Erez Perelman, Jeremy Lau, Brad Cald...
94
Voted
UAI
2008
15 years 2 months ago
New Techniques for Algorithm Portfolio Design
We present and evaluate new techniques for designing algorithm portfolios. In our view, the problem has both a scheduling aspect and a machine learning aspect. Prior work has larg...
Matthew J. Streeter, Stephen F. Smith
161
Voted
VL
2010
IEEE
216views Visual Languages» more  VL 2010»
14 years 11 months ago
Explanatory Debugging: Supporting End-User Debugging of Machine-Learned Programs
Many machine-learning algorithms learn rules of behavior from individual end users, such as taskoriented desktop organizers and handwriting recognizers. These rules form a “prog...
Todd Kulesza, Simone Stumpf, Margaret M. Burnett, ...
106
Voted
ISI
2007
Springer
15 years 19 days ago
Host Based Intrusion Detection using Machine Learning
—Detecting unknown malicious code (malcode) is a challenging task. Current common solutions, such as anti-virus tools, rely heavily on prior explicit knowledge of specific instan...
Robert Moskovitch, Shay Pluderman, Ido Gus, Dima S...
109
Voted
ECML
2005
Springer
15 years 6 months ago
U-Likelihood and U-Updating Algorithms: Statistical Inference in Latent Variable Models
Abstract. In this paper we consider latent variable models and introduce a new U-likelihood concept for estimating the distribution over hidden variables. One can derive an estimat...
JaeMo Sung, Sung Yang Bang, Seungjin Choi, Zoubin ...