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130
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PKDD
2010
Springer
158views Data Mining» more  PKDD 2010»
15 years 2 months ago
Learning Sparse Gaussian Markov Networks Using a Greedy Coordinate Ascent Approach
In this paper, we introduce a simple but efficient greedy algorithm, called SINCO, for the Sparse INverse COvariance selection problem, which is equivalent to learning a sparse Ga...
Katya Scheinberg, Irina Rish
98
Voted
ICDM
2006
IEEE
84views Data Mining» more  ICDM 2006»
15 years 9 months ago
Exploratory Under-Sampling for Class-Imbalance Learning
Under-sampling is a class-imbalance learning method which uses only a subset of major class examples and thus is very efficient. The main deficiency is that many major class exa...
Xu-Ying Liu, Jianxin Wu, Zhi-Hua Zhou
109
Voted
ALT
1998
Springer
15 years 8 months ago
Predictive Learning Models for Concept Drift
Concept drift means that the concept about which data is obtained may shift from time to time, each time after some minimum permanence. Except for this minimum permanence, the con...
John Case, Sanjay Jain, Susanne Kaufmann, Arun Sha...
146
Voted
NIPS
2007
15 years 5 months ago
A Constraint Generation Approach to Learning Stable Linear Dynamical Systems
Stability is a desirable characteristic for linear dynamical systems, but it is often ignored by algorithms that learn these systems from data. We propose a novel method for learn...
Sajid M. Siddiqi, Byron Boots, Geoffrey J. Gordon
129
Voted
CE
2007
96views more  CE 2007»
15 years 3 months ago
Mobile learning: A framework and evaluation
Wireless data communications in form of Short Message Service (SMS) and Wireless Access Protocols (WAP) browsers have gained global popularity, yet, not much has been done to exte...
Luvai F. Motiwalla