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» Evaluating learning algorithms and classifiers
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133
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KDD
2010
ACM
222views Data Mining» more  KDD 2010»
15 years 6 months ago
Large linear classification when data cannot fit in memory
Recent advances in linear classification have shown that for applications such as document classification, the training can be extremely efficient. However, most of the existing t...
Hsiang-Fu Yu, Cho-Jui Hsieh, Kai-Wei Chang, Chih-J...
NAACL
2003
15 years 6 months ago
Automatic Extraction of Semantic Networks from Text using Leximancer
Leximancer is a software system for performing conceptual analysis of text data in a largely language independent manner. The system is modelled on Content Analysis and provides u...
Andrew E. Smith
CVPR
2008
IEEE
16 years 6 months ago
Classification and evaluation of cost aggregation methods for stereo correspondence
In the last decades several cost aggregation methods aimed at improving the robustness of stereo correspondence within local and global algorithms have been proposed. Given the re...
Federico Tombari, Stefano Mattoccia, Luigi di Stef...
ICCV
2005
IEEE
16 years 6 months ago
Probabilistic Boosting-Tree: Learning Discriminative Models for Classification, Recognition, and Clustering
In this paper, a new learning framework?probabilistic boosting-tree (PBT), is proposed for learning two-class and multi-class discriminative models. In the learning stage, the pro...
Zhuowen Tu
125
Voted
ICDAR
2003
IEEE
15 years 10 months ago
Rejection Algorithm for Mis-segmented Characters In Multilingual Document Recognition
In OCR systems the character segmentation algorithm may generate mis-segmented blocks. Feedback information from character classifier is indispensable to achieve higher character ...
Zhengang Chen, Xiaoqing Ding