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» Machine-Learning Applications of Algorithmic Randomness
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ICML
2010
IEEE
13 years 9 months ago
Conditional Topic Random Fields
Generative topic models such as LDA are limited by their inability to utilize nontrivial input features to enhance their performance, and many topic models assume that topic assig...
Jun Zhu, Eric P. Xing
FIMH
2009
Springer
13 years 5 months ago
Random Forest Classification for Automatic Delineation of Myocardium in Real-Time 3D Echocardiography
Automatic delineation of the myocardium in real-time 3D echocardiography may be used to aid the diagnosis of heart problems such as ischaemia, by enabling quantification of wall th...
Victor S. Lempitsky, Michael Verhoek, J. Alison No...
ICML
2007
IEEE
14 years 8 months ago
Conditional random fields for multi-agent reinforcement learning
Conditional random fields (CRFs) are graphical models for modeling the probability of labels given the observations. They have traditionally been trained with using a set of obser...
Xinhua Zhang, Douglas Aberdeen, S. V. N. Vishwanat...
ICML
2004
IEEE
14 years 8 months ago
Learning random walk models for inducing word dependency distributions
Many NLP tasks rely on accurately estimating word dependency probabilities P(w1|w2), where the words w1 and w2 have a particular relationship (such as verb-object). Because of the...
Kristina Toutanova, Christopher D. Manning, Andrew...
ICML
2009
IEEE
14 years 2 months ago
Sparse higher order conditional random fields for improved sequence labeling
In real sequence labeling tasks, statistics of many higher order features are not sufficient due to the training data sparseness, very few of them are useful. We describe Sparse H...
Xian Qian, Xiaoqian Jiang, Qi Zhang, Xuanjing Huan...