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» On the Convergence of Boosting Procedures
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EC
2002
228views ECommerce» more  EC 2002»
13 years 8 months ago
Improved Sampling of the Pareto-Front in Multiobjective Genetic Optimizations by Steady-State Evolution: A Pareto Converging Gen
Previous work on multiobjective genetic algorithms has been focused on preventing genetic drift and the issue of convergence has been given little attention. In this paper, we pre...
Rajeev Kumar, Peter Rockett
ICML
2000
IEEE
14 years 9 months ago
Rates of Convergence for Variable Resolution Schemes in Optimal Control
This paper presents a general method to derive tight rates of convergence for numerical approximations in optimal control when we consider variable resolution grids. We study the ...
Andrew W. Moore, Rémi Munos
SIGMOD
2001
ACM
200views Database» more  SIGMOD 2001»
14 years 8 months ago
Data Bubbles: Quality Preserving Performance Boosting for Hierarchical Clustering
In this paper, we investigate how to scale hierarchical clustering methods (such as OPTICS) to extremely large databases by utilizing data compression methods (such as BIRCH or ra...
Markus M. Breunig, Hans-Peter Kriegel, Peer Kr&oum...
NIPS
2003
13 years 10 months ago
Margin Maximizing Loss Functions
Margin maximizing properties play an important role in the analysis of classi£cation models, such as boosting and support vector machines. Margin maximization is theoretically in...
Saharon Rosset, Ji Zhu, Trevor Hastie
ISBI
2011
IEEE
13 years 9 days ago
Hippocampus segmentation using a stable maximum likelihood classifier ensemble algorithm
We develop a new algorithm to segment the hippocampus from MR images. Our method uses a new classifier ensemble algorithm to correct segmentation errors produced by a multi-atlas...
Hongzhi Wang, Jung Wook Suh, Sandhitsu R. Das, Mur...