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Multi-label classification

Known as: Algorithm adaptation methods, Multi-label, RAKEL 
In machine learning, multi-label classification and the strongly related problem of multi-output classification are variants of the classification… 
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Papers overview

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2017
2017
Multi-label text classification is the process of assigning multi-labels to an instance. A significant aspect of the text… 
2016
2016
24th Irish Conference on Artificial Intelligence and Cognitive Science (AICS'16), Dublin, Ireland, 20-21 September 2016 
2014
2014
We describe a system for robustly estimating synthetic depth maps in unconstrained images and videos, for semi-automatic… 
2014
2014
Dealing with multiple labels is a supervised learning problem of increasing importance. Multi-label classifiers face the… 
Highly Cited
2013
Highly Cited
2013
We present GURLS, a least squares, modular, easy-to-extend software library for efficient supervised learning. GURLS is targeted… 
2013
2013
The imbalanced sentiment distribution of microblogs induces bad performance of binary classifiers on the minority class. To… 
2011
2011
The paper presents an approach to the task of automatic document categorization in the field of economics. Since the documents… 
2010
2010
Multi-task learning aims at combining information across tasks to boost prediction performance, especially when the number of… 
2008
2008
Multi-label problems arise in various domains such as multi-topic web page categorization, protein function prediction, and…