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CSB
2005
IEEE
189views Bioinformatics» more  CSB 2005»
15 years 10 months ago
Learning Yeast Gene Functions from Heterogeneous Sources of Data Using Hybrid Weighted Bayesian Networks
We developed a machine learning system for determining gene functions from heterogeneous sources of data sets using a Weighted Naive Bayesian Network (WNB). The knowledge of gene ...
Xutao Deng, Huimin Geng, Hesham H. Ali
FLAIRS
2007
15 years 6 months ago
Explaining Task Processing in Cognitive Assistants that Learn
As personal assistant software matures and assumes more autonomous control of its users’ activities, it becomes more critical that this software can explain its task processing....
Deborah L. McGuinness, Alyssa Glass, Michael Wolve...
SDM
2008
SIAM
105views Data Mining» more  SDM 2008»
15 years 6 months ago
Gaussian Process Learning for Cyber-Attack Early Warning
Network security has been a serious concern for many years. For example, firewalls often record thousands of exploit attempts on a daily basis. Network administrators could benefi...
Jian Zhang 0004, Phillip A. Porras, Johannes Ullri...
AAAI
1998
15 years 5 months ago
Learning Investment Functions for Controlling the Utility of Control Knowledge
The utility problem occurs when the cost of the acquired knowledge outweighs its bene ts. When the learner acquires control knowledge for speeding up a problem solver, the bene t ...
Oleg Ledeniov, Shaul Markovitch
NIPS
1998
15 years 5 months ago
Learning Nonlinear Dynamical Systems Using an EM Algorithm
The Expectation Maximization EM algorithm is an iterative procedure for maximum likelihood parameter estimation from data sets with missing or hidden variables 2 . It has been app...
Zoubin Ghahramani, Sam T. Roweis