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ICPR
2006
IEEE
14 years 9 months ago
Combining Generative and Discriminative Methods for Pixel Classification with Multi-Conditional Learning
It is possible to broadly characterize two approaches to probabilistic modeling in terms of generative and discriminative methods. Provided with sufficient training data the discr...
B. Michael Kelm, Chris Pal, Andrew McCallum
ACL
2003
13 years 10 months ago
Text Chunking by Combining Hand-Crafted Rules and Memory-Based Learning
This paper proposes a hybrid of handcrafted rules and a machine learning method for chunking Korean. In the partially free word-order languages such as Korean and Japanese, a smal...
Seong-Bae Park, Byoung-Tak Zhang
ICML
2001
IEEE
14 years 9 months ago
Smoothed Bootstrap and Statistical Data Cloning for Classifier Evaluation
This work is concerned with the estimation of a classifier's accuracy. We first review some existing methods for error estimation, focusing on cross-validation and bootstrap,...
Gregory Shakhnarovich, Ran El-Yaniv, Yoram Baram
EMMCVPR
2005
Springer
14 years 2 months ago
Exploiting Inference for Approximate Parameter Learning in Discriminative Fields: An Empirical Study
Abstract. Estimation of parameters of random field models from labeled training data is crucial for their good performance in many image analysis applications. In this paper, we p...
Sanjiv Kumar, Jonas August, Martial Hebert
TIP
2010
123views more  TIP 2010»
13 years 7 months ago
Optimizing Motion Compensated Prediction for Error Resilient Video Coding
—This paper is concerned with optimization of the motion compensated prediction framework to improve the error resilience of video coding for transmission over lossy networks. Fi...
Hua Yang, Kenneth Rose