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» Apprenticeship learning using linear programming
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ICML
2010
IEEE
14 years 10 months ago
Boosting Classifiers with Tightened L0-Relaxation Penalties
We propose a novel boosting algorithm which improves on current algorithms for weighted voting classification by striking a better balance between classification accuracy and the ...
Noam Goldberg, Jonathan Eckstein
CEC
2010
IEEE
14 years 1 months ago
Tweaking a tower of blocks leads to a TMBL: Pursuing long term fitness growth in program evolution
— If a population of programs evolved not for a few hundred generations but for a few hundred thousand or more, could it generate more interesting behaviours and tackle more comp...
Tony E. Lewis, George D. Magoulas
JMLR
2012
12 years 12 months ago
Multi Kernel Learning with Online-Batch Optimization
In recent years there has been a lot of interest in designing principled classification algorithms over multiple cues, based on the intuitive notion that using more features shou...
Francesco Orabona, Jie Luo, Barbara Caputo
CORR
2007
Springer
126views Education» more  CORR 2007»
14 years 9 months ago
Linear Tabling Strategies and Optimizations
Recently there has been a growing interest of research in tabling in the logic programming community because of its usefulness in a variety of application domains including progra...
Neng-Fa Zhou, Taisuke Sato, Yi-Dong Shen
84
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SEAL
1998
Springer
15 years 1 months ago
Genetic Programming with Active Data Selection
Genetic programming evolves Lisp-like programs rather than fixed size linear strings. This representational power combined with generality makes genetic programming an interesting ...
Byoung-Tak Zhang, Dong-Yeon Cho