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» Learning Mid-Level Features For Recognition
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NIPS
2008
13 years 9 months ago
Regularized Learning with Networks of Features
For many supervised learning problems, we possess prior knowledge about which features yield similar information about the target variable. In predicting the topic of a document, ...
Ted Sandler, John Blitzer, Partha Pratim Talukdar,...
CVPR
2005
IEEE
14 years 1 months ago
Jensen-Shannon Boosting Learning for Object Recognition
In this paper, we propose a novel learning method, called Jensen-Shannon Boosting (JSBoost) and demonstrate its application to object recognition. JSBoost incorporates Jensen-Shan...
Xiangsheng Huang, Stan Z. Li, Yangsheng Wang
ICANN
2005
Springer
14 years 1 months ago
Online Learning for Object Recognition with a Hierarchical Visual Cortex Model
We present an architecture for the online learning of object representations based on a visual cortex hierarchy developed earlier. We use the output of a topographical feature hier...
Stephan Kirstein, Heiko Wersing, Edgar Körner
BMCBI
2005
134views more  BMCBI 2005»
13 years 7 months ago
Systematic feature evaluation for gene name recognition
In task 1A of the BioCreAtIvE evaluation, systems had to be devised that recognize words and phrases forming gene or protein names in natural language sentences. We approach this ...
Jörg Hakenberg, Steffen Bickel, Conrad Plake,...
ICDM
2009
IEEE
233views Data Mining» more  ICDM 2009»
14 years 2 months ago
Semi-Supervised Sequence Labeling with Self-Learned Features
—Typical information extraction (IE) systems can be seen as tasks assigning labels to words in a natural language sequence. The performance is restricted by the availability of l...
Yanjun Qi, Pavel Kuksa, Ronan Collobert, Kunihiko ...