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» On learning with dissimilarity functions
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ICML
2006
IEEE
14 years 10 months ago
A graphical model for predicting protein molecular function
We present a simple statistical model of molecular function evolution to predict protein function. The model description encodes general knowledge of how molecular function evolve...
Barbara E. Engelhardt, Michael I. Jordan, Steven E...
ICTAI
2006
IEEE
14 years 3 months ago
Polynomial Regression with Automated Degree: A Function Approximator for Autonomous Agents
In order for an autonomous agent to behave robustly in a variety of environments, it must have the ability to learn approximations to many different functions. The function approx...
Daniel Stronger, Peter Stone
KDD
2005
ACM
109views Data Mining» more  KDD 2005»
14 years 10 months ago
Formulating distance functions via the kernel trick
Tasks of data mining and information retrieval depend on a good distance function for measuring similarity between data instances. The most effective distance function must be for...
Gang Wu, Edward Y. Chang, Navneet Panda
ML
2008
ACM
115views Machine Learning» more  ML 2008»
13 years 9 months ago
Calling variadic functions from a strongly-typed language
The importance of providing a mechanism to call C functions from high-level languages has been understood for many years and, these days, almost all statically-typed high-level-la...
Matthias Blume, Mike Rainey, John H. Reppy
ICML
1998
IEEE
14 years 10 months ago
Value Function Based Production Scheduling
Production scheduling, the problem of sequentially con guring a factory to meet forecasted demands, is a critical problem throughout the manufacturing industry. The requirement of...
Jeff G. Schneider, Justin A. Boyan, Andrew W. Moor...