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» Predictive Hebbian Learning
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187
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RECOMB
2004
Springer
16 years 3 months ago
Modeling and Analysis of Heterogeneous Regulation in Biological Networks
Abstract. In this study we propose a novel model for the representation of biological networks and provide algorithms for learning model parameters from experimental data. Our appr...
Irit Gat-Viks, Amos Tanay, Ron Shamir
168
Voted
JMLR
2008
230views more  JMLR 2008»
15 years 3 months ago
Exponentiated Gradient Algorithms for Conditional Random Fields and Max-Margin Markov Networks
Log-linear and maximum-margin models are two commonly-used methods in supervised machine learning, and are frequently used in structured prediction problems. Efficient learning of...
Michael Collins, Amir Globerson, Terry Koo, Xavier...
140
Voted
MSOM
2007
118views more  MSOM 2007»
15 years 3 months ago
What Can Be Learned from Classical Inventory Models? A Cross-Industry Exploratory Investigation
: Classical inventory models offer a variety of insights into the optimal way to manage inventories of individual products. However, top managers and industry analysts are often co...
Sergey Rumyantsev, Serguei Netessine
148
Voted
ICDM
2010
IEEE
273views Data Mining» more  ICDM 2010»
15 years 1 months ago
Learning Maximum Lag for Grouped Graphical Granger Models
Temporal causal modeling has been a highly active research area in the last few decades. Temporal or time series data arises in a wide array of application domains ranging from med...
Amit Dhurandhar
186
Voted
COLT
1994
Springer
15 years 7 months ago
Bayesian Inductive Logic Programming
Inductive Logic Programming (ILP) involves the construction of first-order definite clause theories from examples and background knowledge. Unlike both traditional Machine Learnin...
Stephen Muggleton