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BMCBI
2011
13 years 5 days ago
Learning sparse models for a dynamic Bayesian network classifier of protein secondary structure
Background: Protein secondary structure prediction provides insight into protein function and is a valuable preliminary step for predicting the 3D structure of a protein. Dynamic ...
Zafer Aydin, Ajit Singh, Jeff Bilmes, William Staf...
JMLR
2008
94views more  JMLR 2008»
13 years 8 months ago
Using Markov Blankets for Causal Structure Learning
We show how a generic feature selection algorithm returning strongly relevant variables can be turned into a causal structure learning algorithm. We prove this under the Faithfuln...
Jean-Philippe Pellet, André Elisseeff
KDD
2009
ACM
181views Data Mining» more  KDD 2009»
14 years 3 months ago
An exploration of climate data using complex networks
To discover patterns in historical data, climate scientists have applied various clustering methods with the goal of identifying regions that share some common climatological beha...
Karsten Steinhaeuser, Nitesh V. Chawla, Auroop R. ...
NIPS
2007
13 years 10 months ago
A Risk Minimization Principle for a Class of Parzen Estimators
This paper1 explores the use of a Maximal Average Margin (MAM) optimality principle for the design of learning algorithms. It is shown that the application of this risk minimizati...
Kristiaan Pelckmans, Johan A. K. Suykens, Bart De ...
ICMLA
2008
13 years 10 months ago
Tumor Targeting for Lung Cancer Radiotherapy Using Machine Learning Techniques
Accurate lung tumor targeting in real time plays a fundamental role in image-guide radiotherapy of lung cancers. Precise tumor targeting is required for both respiratory gating an...
Tong Lin, Laura Cervino, Xiaoli Tang, Nuno Vasconc...