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ICML
2009
IEEE
14 years 9 months ago
Detecting the direction of causal time series
We propose a method that detects the true direction of time series, by fitting an autoregressive moving average model to the data. Whenever the noise is independent of the previou...
Arthur Gretton, Bernhard Schölkopf, Dominik J...
JMLR
2002
87views more  JMLR 2002»
13 years 8 months ago
Shallow Parsing using Noisy and Non-Stationary Training Material
Shallow parsers are usually assumed to be trained on noise-free material, drawn from the same distribution as the testing material. However, when either the training set is noisy ...
Miles Osborne
ECML
2007
Springer
14 years 3 months ago
Learning Balls of Strings with Correction Queries
When facing the question of learning languages in realistic settings, one has to tackle several problems that do not admit simple solutions. On the one hand, languages are usually...
Leonor Becerra-Bonache, Colin de la Higuera, Jean-...
ICDM
2005
IEEE
122views Data Mining» more  ICDM 2005»
14 years 2 months ago
Learning through Changes: An Empirical Study of Dynamic Behaviors of Probability Estimation Trees
In practice, learning from data is often hampered by the limited training examples. In this paper, as the size of training data varies, we empirically investigate several probabil...
Kun Zhang, Zujia Xu, Jing Peng, Bill P. Buckles
ICPR
2010
IEEE
14 years 1 months ago
Learning an Efficient and Robust Graph Matching Procedure for Specific Object Recognition
We present a fast and robust graph matching approach for 2D specific object recognition in images. From a small number of training images, a model graph of the object to learn is a...
Jerome Revaud, Guillaume Lavoue, Yasuo Ariki, Atil...