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JMLR
2012
13 years 7 months ago
SpeedBoost: Anytime Prediction with Uniform Near-Optimality
We present SpeedBoost, a natural extension of functional gradient descent, for learning anytime predictors, which automatically trade computation time for predictive accuracy by s...
Alexander Grubb, Drew Bagnell
GECCO
2007
Springer
177views Optimization» more  GECCO 2007»
15 years 11 months ago
Evolutionary algorithms and matroid optimization problems
We analyze the performance of evolutionary algorithms on various matroid optimization problems that encompass a vast number of efficiently solvable as well as NP-hard combinatoria...
Joachim Reichel, Martin Skutella
STOC
2004
ACM
138views Algorithms» more  STOC 2004»
15 years 10 months ago
Know thy neighbor's neighbor: the power of lookahead in randomized P2P networks
Several peer-to-peer networks are based upon randomized graph topologies that permit efficient greedy routing, e.g., randomized hypercubes, randomized Chord, skip-graphs and const...
Gurmeet Singh Manku, Moni Naor, Udi Wieder
ECCV
2010
Springer
15 years 5 months ago
MIForests: Multiple-Instance Learning with Randomized Trees
Abstract. Multiple-instance learning (MIL) allows for training classifiers from ambiguously labeled data. In computer vision, this learning paradigm has been recently used in many ...
Christian Leistner, Amir Saffari, Horst Bischof
PPSN
2004
Springer
15 years 10 months ago
Optimization via Parameter Mapping with Genetic Programming
Abstract. This paper describes a new approach for parameter optimization that uses a novel representation for the parameters to be optimized. By using genetic programming, the new ...
João Carlos Figueira Pujol, Riccardo Poli