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ACL
2006
15 years 4 months ago
Semi-Supervised Conditional Random Fields for Improved Sequence Segmentation and Labeling
We present a new semi-supervised training procedure for conditional random fields (CRFs) that can be used to train sequence segmentors and labelers from a combination of labeled a...
Feng Jiao, Shaojun Wang, Chi-Hoon Lee, Russell Gre...
ICCV
2009
IEEE
16 years 8 months ago
Semi-Supervised Random Forests
Random Forests (RFs) have become commonplace in many computer vision applications. Their popularity is mainly driven by their high computational efficiency during both training ...
Christian Leistner, Amir Saffari, Jakob Santner, H...
CC
2008
Springer
131views System Software» more  CC 2008»
15 years 3 months ago
Exposure-Resilient Extractors and the Derandomization of Probabilistic Sublinear Time
There exists a positive constant < 1 such that for any function T(n) n and for any problem L BPTIME(T(n)), there exists a deterministic algorithm running in poly(T(n)) time w...
Marius Zimand
APPROX
2008
Springer
100views Algorithms» more  APPROX 2008»
15 years 5 months ago
Optimal Random Matchings on Trees and Applications
In this paper we will consider tight upper- and lower-bounds on the weight of the optimal matching for random point sets distributed among the leaves of a tree, as a function of it...
Jeff Abrahamson, Béla Csaba, Ali Shokoufand...
147
Voted
PAMI
2008
270views more  PAMI 2008»
15 years 3 months ago
Randomized Clustering Forests for Image Classification
This paper introduces three new contributions to the problems of image classification and image search. First, we propose a new image patch quantization algorithm. Other competitiv...
Frank Moosmann, Eric Nowak, Frédéric...