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ICML
2006
IEEE
16 years 7 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...
ICML
1999
IEEE
16 years 7 months ago
Abstracting from Robot Sensor Data using Hidden Markov Models
ing from Robot Sensor Data using Hidden Markov Models Laura Firoiu, Paul Cohen Computer Science Department, LGRC University of Massachusetts at Amherst, Box 34610 Amherst, MA 01003...
Laura Firoiu, Paul R. Cohen
AII
1992
15 years 11 months ago
Learning from Multiple Sources of Inaccurate Data
Most theoretical models of inductive inference make the idealized assumption that the data available to a learner is from a single and accurate source. The subject of inaccuracies ...
Ganesh Baliga, Sanjay Jain, Arun Sharma
ICML
2007
IEEE
16 years 7 months ago
Scalable training of L1-regularized log-linear models
The l-bfgs limited-memory quasi-Newton method is the algorithm of choice for optimizing the parameters of large-scale log-linear models with L2 regularization, but it cannot be us...
Galen Andrew, Jianfeng Gao
ICML
2006
IEEE
16 years 7 months ago
Convex optimization techniques for fitting sparse Gaussian graphical models
We consider the problem of fitting a large-scale covariance matrix to multivariate Gaussian data in such a way that the inverse is sparse, thus providing model selection. Beginnin...
Onureena Banerjee, Laurent El Ghaoui, Alexandre d'...