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» Machine-Learning Applications of Algorithmic Randomness
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ICML
2007
IEEE
14 years 8 months ago
Comparisons of sequence labeling algorithms and extensions
In this paper, we survey the current state-ofart models for structured learning problems, including Hidden Markov Model (HMM), Conditional Random Fields (CRF), Averaged Perceptron...
Nam Nguyen, Yunsong Guo
EMNLP
2009
13 years 5 months ago
On the Use of Virtual Evidence in Conditional Random Fields
Virtual evidence (VE), first introduced by (Pearl, 1988), provides a convenient way of incorporating prior knowledge into Bayesian networks. This work generalizes the use of VE to...
Xiao Li
ICML
2007
IEEE
14 years 8 months ago
Gradient boosting for kernelized output spaces
A general framework is proposed for gradient boosting in supervised learning problems where the loss function is defined using a kernel over the output space. It extends boosting ...
Florence d'Alché-Buc, Louis Wehenkel, Pierr...
ICML
2006
IEEE
14 years 8 months ago
Fast direct policy evaluation using multiscale analysis of Markov diffusion processes
Policy evaluation is a critical step in the approximate solution of large Markov decision processes (MDPs), typically requiring O(|S|3 ) to directly solve the Bellman system of |S...
Mauro Maggioni, Sridhar Mahadevan
ICML
2000
IEEE
14 years 8 months ago
Complete Cross-Validation for Nearest Neighbor Classifiers
Cross-validation is an established technique for estimating the accuracy of a classifier and is normally performed either using a number of random test/train partitions of the dat...
Matthew D. Mullin, Rahul Sukthankar