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» Learning from Highly Structured Data by Decomposition
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AAAI
2000
14 years 11 months ago
Learning the Common Structure of Data
The proliferation of online information sources has accentuated the need for tools that automatically validate and recognize data. We present an efficient algorithm that learns st...
Kristina Lerman, Steven Minton
DCG
2008
104views more  DCG 2008»
14 years 9 months ago
Finding the Homology of Submanifolds with High Confidence from Random Samples
Recently there has been a lot of interest in geometrically motivated approaches to data analysis in high dimensional spaces. We consider the case where data is drawn from sampling...
Partha Niyogi, Stephen Smale, Shmuel Weinberger
CSB
2004
IEEE
177views Bioinformatics» more  CSB 2004»
15 years 1 months ago
High-Throughput 3D Structural Homology Detection via NMR Resonance Assignment
One goal of the structural genomics initiative is the identification of new protein folds. Sequence-based structural homology prediction methods are an important means for priorit...
Christopher James Langmead, Bruce Randall Donald
IDA
2003
Springer
15 years 2 months ago
Learning Dynamic Bayesian Networks from Multivariate Time Series with Changing Dependencies
Abstract. Many examples exist of multivariate time series where dependencies between variables change over time. If these changing dependencies are not taken into account, any mode...
Allan Tucker, Xiaohui Liu
BMCBI
2007
215views more  BMCBI 2007»
14 years 9 months ago
Learning causal networks from systems biology time course data: an effective model selection procedure for the vector autoregres
Background: Causal networks based on the vector autoregressive (VAR) process are a promising statistical tool for modeling regulatory interactions in a cell. However, learning the...
Rainer Opgen-Rhein, Korbinian Strimmer