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ICML
2009
IEEE
14 years 8 months ago
Importance weighted active learning
We propose an importance weighting framework for actively labeling samples. This technique yields practical yet sound active learning algorithms for general loss functions. Experi...
Alina Beygelzimer, Sanjoy Dasgupta, John Langford
APPROX
2006
Springer
234views Algorithms» more  APPROX 2006»
13 years 11 months ago
Constant-Factor Approximation for Minimum-Weight (Connected) Dominating Sets in Unit Disk Graphs
For a given graph with weighted vertices, the goal of the minimum-weight dominating set problem is to compute a vertex subset of smallest weight such that each vertex of the graph...
Christoph Ambühl, Thomas Erlebach, Matú...
DCC
2010
IEEE
14 years 1 months ago
Auto Regressive Model and Weighted Least Squares Based Packet Video Error Concealment
In this paper, auto regressive (AR) model is applied to error concealment for block-based packet video encoding. Each pixel within the corrupted block is restored as the weighted ...
Yongbing Zhang, Xinguang Xiang, Siwei Ma, Debin Zh...
FLAIRS
2004
13 years 9 months ago
Hidden Layer Training via Hessian Matrix Information
The output weight optimization-hidden weight optimization (OWO-HWO) algorithm for training the multilayer perceptron alternately updates the output weights and the hidden weights....
Changhua Yu, Michael T. Manry, Jiang Li
FAW
2009
Springer
134views Algorithms» more  FAW 2009»
14 years 2 months ago
Pathwidth is NP-Hard for Weighted Trees
The pathwidth of a graph G is the minimum clique number of H minus one, over all interval supergraphs H of G. We prove in this paper that the PATHWIDTH problem is NP-hard for parti...
Rodica Mihai, Ioan Todinca