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» Boosting and Maximum Likelihood for Exponential Models
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ICML
1999
IEEE
14 years 8 months ago
Monte Carlo Hidden Markov Models: Learning Non-Parametric Models of Partially Observable Stochastic Processes
We present a learning algorithm for non-parametric hidden Markov models with continuous state and observation spaces. All necessary probability densities are approximated using sa...
Sebastian Thrun, John Langford, Dieter Fox
ICASSP
2008
IEEE
14 years 1 months ago
Symbol graph based discriminative training and rescoring for improved math symbol recognition
In the symbol recognition stage of online handwritten math expression recognition, the one-pass dynamic programming algorithm can produce high-quality symbol graphs in addition of...
Zhen Xuan Luo, Yu Shi, Frank K. Soong
KDD
2001
ACM
163views Data Mining» more  KDD 2001»
14 years 7 months ago
The "DGX" distribution for mining massive, skewed data
Skewed distributions appear very often in practice. Unfortunately, the traditional Zipf distribution often fails to model them well. In this paper, we propose a new probability di...
Zhiqiang Bi, Christos Faloutsos, Flip Korn
CVPR
2004
IEEE
14 years 9 months ago
A Unified Framework for Uncertainty Propagation in Automatic Shape Tracking
Uncertainty handling plays an important role during shape tracking. We have recently shown that the fusion of measurement information with system dynamics and shape priors greatly...
Xiang Sean Zhou, Dorin Comaniciu, Binglong Xie, R....
ICASSP
2011
IEEE
12 years 11 months ago
Online feature selection and classification
This paper presents an online feature selection and classification algorithm. The algorithm is implemented for impact acoustics signals to sort hazelnut kernels. The classifier, w...
Habil Kalkan, Bayram Cetisli