Multilabel Neural Networks with Applications to Functional Genomics and Text Categorization

In multilabel learning, each instance in the training set is associated with a set of labels and the task is to output a label set whose size is unknown a priori for each unseen instance. In this paper, this problem is addressed in the way that a neural network algorithm named BP-MLL, i.e., backprop...

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Veröffentlicht in:	IEEE transactions on knowledge and data engineering Jg. 18; H. 10; S. 1338 - 1351
Hauptverfasser:	ZHANG, Min-Ling, ZHOU, Zhi-Hua
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	New York, NY IEEE 01.10.2006 IEEE Computer Society The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Schlagworte:	Algorithms Applied sciences Artificial intelligence Back propagation backpropagation Backpropagation algorithms Biochemistry Bioinformatics Computer science; control theory; systems Connectionism. Neural networks Data mining Error functions Exact sciences and technology functional genomics Genomics Government Information systems. Data bases Labels Layout Learning Learning systems Machine learning Memory organisation. Data processing multilabel learning Neural networks Software Tasks Text categorization Texts Genomics neural networks text categorization Information extraction functional genomics multilabel learning Neural network Data mining Backpropagation algorithm Machine learning backpropagation Linguistics Learning algorithm
ISSN:	1041-4347, 1558-2191
Online-Zugang:	Volltext
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