Training products of experts by minimizing contrastive divergence
Neural Computation
Correlated Label Propagation with Application to Multi-label Learning
CVPR '06 Proceedings of the 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition - Volume 2
ML-KNN: A lazy learning approach to multi-label learning
Pattern Recognition
Restricted Boltzmann machines for collaborative filtering
Proceedings of the 24th international conference on Machine learning
Classification using discriminative restricted Boltzmann machines
Proceedings of the 25th international conference on Machine learning
Introduction to Information Retrieval
Introduction to Information Retrieval
Putting Objects in Perspective
International Journal of Computer Vision
Random k-Labelsets: An Ensemble Method for Multilabel Classification
ECML '07 Proceedings of the 18th European conference on Machine Learning
Learning Spatial Context: Using Stuff to Find Things
ECCV '08 Proceedings of the 10th European Conference on Computer Vision: Part I
Evaluating similarity measures for emergent semantics of social tagging
Proceedings of the 18th international conference on World wide web
Cheap and fast---but is it good?: evaluating non-expert annotations for natural language tasks
EMNLP '08 Proceedings of the Conference on Empirical Methods in Natural Language Processing
Folks in Folksonomies: social link prediction from shared metadata
Proceedings of the third ACM international conference on Web search and data mining
Exploring automatic music annotation with "acoustically-objective" tags
Proceedings of the international conference on Multimedia information retrieval
MULAN: A Java Library for Multi-Label Learning
The Journal of Machine Learning Research
Learning algorithms for the classification restricted Boltzmann machine
The Journal of Machine Learning Research
MusiClef: multimodal music tagging task
CLEF'12 Proceedings of the Third international conference on Information Access Evaluation: multilinguality, multimodality, and visual analytics
Location-aware music recommendation using auto-tagging and hybrid matching
Proceedings of the 7th ACM conference on Recommender systems
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This article examines the use of two kinds of context to improve the results of content-based music taggers: the relationships between tags and between the clips of songs that are tagged. We show that users agree more on tags applied to clips temporally “closer” to one another; that conditional restricted Boltzmann machine models of tags can more accurately predict related tags when they take context into account; and that when training data is “smoothed” using context, support vector machines can better rank these clips according to the original, unsmoothed tags and do this more accurately than three standard multi-label classifiers.