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131
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GECCO
2007
Springer
156views Optimization» more  GECCO 2007»
15 years 9 months ago
Techniques for highly multiobjective optimisation: some nondominated points are better than others
The research area of evolutionary multiobjective optimization (EMO) is reaching better understandings of the properties and capabilities of EMO algorithms, and accumulating much e...
David W. Corne, Joshua D. Knowles
148
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JMLR
2010
143views more  JMLR 2010»
15 years 1 months ago
A Quasi-Newton Approach to Nonsmooth Convex Optimization Problems in Machine Learning
We extend the well-known BFGS quasi-Newton method and its memory-limited variant LBFGS to the optimization of nonsmooth convex objectives. This is done in a rigorous fashion by ge...
Jin Yu, S. V. N. Vishwanathan, Simon Günter, ...
134
Voted
BCB
2010
138views Bioinformatics» more  BCB 2010»
14 years 9 months ago
Comparative analysis of biclustering algorithms
Biclustering is a very popular method to identify hidden co-regulation patterns among genes. There are numerous biclustering algorithms designed to undertake this challenging task...
Doruk Bozdag, Ashwin S. Kumar, Ümit V. &Ccedi...
136
Voted
GECCO
2007
Springer
182views Optimization» more  GECCO 2007»
15 years 9 months ago
An analysis of the effects of population structure on scalable multiobjective optimization problems
Multiobjective evolutionary algorithms (MOEA) are an effective tool for solving search and optimization problems containing several incommensurable and possibly conflicting objec...
Michael Kirley, Robert L. Stewart
127
Voted
ICML
1998
IEEE
16 years 3 months ago
An Efficient Boosting Algorithm for Combining Preferences
We study the problem of learning to accurately rank a set of objects by combining a given collection of ranking or preference functions. This problem of combining preferences aris...
Yoav Freund, Raj D. Iyer, Robert E. Schapire, Yora...