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» Evaluating machine learning for information extraction
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ICML
2004
IEEE
16 years 5 months ago
Training conditional random fields via gradient tree boosting
Conditional Random Fields (CRFs; Lafferty, McCallum, & Pereira, 2001) provide a flexible and powerful model for learning to assign labels to elements of sequences in such appl...
Thomas G. Dietterich, Adam Ashenfelter, Yaroslav B...
DOCENG
2006
ACM
15 years 10 months ago
NEWPAR: an automatic feature selection and weighting schema for category ranking
Category ranking provides a way to classify plain text documents into a pre-determined set of categories. This work proposes to have a look at typical document collections and ana...
Fernando Ruiz-Rico, José Luis Vicedo Gonz&a...
COLING
2010
14 years 11 months ago
An Empirical Study on Web Mining of Parallel Data
This paper1 presents an empirical approach to mining parallel corpora. Conventional approaches use a readily available collection of comparable, nonparallel corpora to extract par...
Gum-Won Hong, Chi-Ho Li, Ming Zhou, Hae-Chang Rim
WAPCV
2007
Springer
15 years 10 months ago
Reinforcement Learning for Decision Making in Sequential Visual Attention
The innovation of this work is the provision of a system that learns visual encodings of attention patterns and that enables sequential attention for object detection in real world...
Lucas Paletta, Gerald Fritz
ECML
2006
Springer
15 years 8 months ago
Cost-Sensitive Decision Tree Learning for Forensic Classification
Abstract. In some learning settings, the cost of acquiring features for classification must be paid up front, before the classifier is evaluated. In this paper, we introduce the fo...
Jason V. Davis, Jungwoo Ha, Christopher J. Rossbac...