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ML
2006
ACM
163views Machine Learning» more  ML 2006»
15 years 6 months ago
Extremely randomized trees
Abstract This paper proposes a new tree-based ensemble method for supervised classification and regression problems. It essentially consists of randomizing strongly both attribute ...
Pierre Geurts, Damien Ernst, Louis Wehenkel
268
Voted
ICCV
2011
IEEE
14 years 6 months ago
Perturb-and-MAP Random Fields: Using Discrete Optimization\\to Learn and Sample from Energy Models
We propose a novel way to induce a random field from an energy function on discrete labels. It amounts to locally injecting noise to the energy potentials, followed by finding t...
George Papandreou, Alan L. Yuille
NDJFL
2010
15 years 1 months ago
An Extension of van Lambalgen's Theorem to Infinitely Many Relative 1-Random Reals
Van Lambalgen's Theorem plays an important role in algorithmic randomness, especially when studying relative randomness. In this paper we extend van Lambalgen's Theorem ...
Kenshi Miyabe
159
Voted
CORR
2010
Springer
134views Education» more  CORR 2010»
15 years 6 months ago
Incremental Sampling-based Algorithms for Optimal Motion Planning
During the last decade, incremental sampling-based motion planning algorithms, such as the Rapidly-exploring Random Trees (RRTs), have been shown to work well in practice and to po...
Sertac Karaman, Emilio Frazzoli
ICALP
1991
Springer
15 years 10 months ago
On-Line Algorithms for Weighted Bipartite Matching and Stable Marriages
We give an on-line deterministic algorithm for the weighted bipartite matching problem that achieves a competitive ratio of (2n − 1) in any metric space (where n is the number o...
Samir Khuller, Stephen G. Mitchell, Vijay V. Vazir...