Learning internal representations by error propagation
Parallel distributed processing: explorations in the microstructure of cognition, vol. 1
Numerical recipes in C (2nd ed.): the art of scientific computing
Numerical recipes in C (2nd ed.): the art of scientific computing
Self-organizing maps
A decision-theoretic generalization of on-line learning and an application to boosting
Journal of Computer and System Sciences - Special issue: 26th annual ACM symposium on the theory of computing & STOC'94, May 23–25, 1994, and second annual Europe an conference on computational learning theory (EuroCOLT'95), March 13–15, 1995
Improved boosting algorithms using confidence-rated predictions
COLT' 98 Proceedings of the eleventh annual conference on Computational learning theory
BoosTexter: A Boosting-based Systemfor Text Categorization
Machine Learning - Special issue on information retrieval
Neural Networks for Pattern Recognition
Neural Networks for Pattern Recognition
A new family of online algorithms for category ranking
SIGIR '02 Proceedings of the 25th annual international ACM SIGIR conference on Research and development in information retrieval
Text Categorization with Suport Vector Machines: Learning with Many Relevant Features
ECML '98 Proceedings of the 10th European Conference on Machine Learning
The Alternating Decision Tree Learning Algorithm
ICML '99 Proceedings of the Sixteenth International Conference on Machine Learning
A maximal figure-of-merit learning approach to text categorization
Proceedings of the 26th annual international ACM SIGIR conference on Research and development in informaion retrieval
A MFoM learning approach to robust multiclass multi-label text categorization
ICML '04 Proceedings of the twenty-first international conference on Machine learning
Hierarchical document categorization with support vector machines
Proceedings of the thirteenth ACM international conference on Information and knowledge management
MMAC: A New Multi-Class, Multi-Label Associative Classification Approach
ICDM '04 Proceedings of the Fourth IEEE International Conference on Data Mining
Multi-labelled classification using maximum entropy method
Proceedings of the 28th annual international ACM SIGIR conference on Research and development in information retrieval
Collective multi-label classification
Proceedings of the 14th ACM international conference on Information and knowledge management
Learning hierarchical multi-category text classification models
ICML '05 Proceedings of the 22nd international conference on Machine learning
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
Multilabel Neural Networks with Applications to Functional Genomics and Text Categorization
IEEE Transactions on Knowledge and Data Engineering
ML-KNN: A lazy learning approach to multi-label learning
Pattern Recognition
Correlative multi-label video annotation
Proceedings of the 15th international conference on Multimedia
A Unified Model for Multilabel Classification and Ranking
Proceedings of the 2006 conference on ECAI 2006: 17th European Conference on Artificial Intelligence August 29 -- September 1, 2006, Riva del Garda, Italy
Semi-supervised multi-label learning by constrained non-negative matrix factorization
AAAI'06 Proceedings of the 21st national conference on Artificial intelligence - Volume 1
IJCAI'07 Proceedings of the 20th international joint conference on Artifical intelligence
Learning multi-label alternating decision trees from texts and data
MLDM'03 Proceedings of the 3rd international conference on Machine learning and data mining in pattern recognition
Feature selection for multi-label naive Bayes classification
Information Sciences: an International Journal
Evidential multi-label classification approach to learning from data with imprecise labels
IPMU'10 Proceedings of the Computational intelligence for knowledge-based systems design, and 13th international conference on Information processing and management of uncertainty
IWANN'11 Proceedings of the 11th international conference on Artificial neural networks conference on Advances in computational intelligence - Volume Part I
ECML PKDD'11 Proceedings of the 2011 European conference on Machine learning and knowledge discovery in databases - Volume Part III
An efficient multi-label support vector machine with a zero label
Expert Systems with Applications: An International Journal
Multi-label weighted k-nearest neighbor classifier with adaptive weight estimation
ICONIP'11 Proceedings of the 18th international conference on Neural Information Processing - Volume Part II
A model for multi-label classification and ranking of learning objects
Expert Systems with Applications: An International Journal
Improving multi-label classifiers via label reduction with association rules
HAIS'12 Proceedings of the 7th international conference on Hybrid Artificial Intelligent Systems - Volume Part II
Fast multi-label core vector machine
Pattern Recognition
Random block coordinate descent method for multi-label support vector machine with a zero label
Expert Systems with Applications: An International Journal
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Multi-label learning deals with the problem where each instance is associated with multiple labels simultaneously. The task of this learning paradigm is to predict the label set for each unseen instance, through analyzing training instances with known label sets. In this paper, a neural network based multi-label learning algorithm named Ml-rbf is proposed, which is derived from the traditional radial basis function (RBF) methods. Briefly, the first layer of an Ml-rbf neural network is formed by conducting clustering analysis on instances of each possible class, where the centroid of each clustered groups is regarded as the prototype vector of a basis function. After that, second layer weights of the Ml-rbf neural network are learned by minimizing a sum-of-squares error function. Specifically, information encoded in the prototype vectors corresponding to all classes are fully exploited to optimize the weights corresponding to each specific class. Experiments on three real-world multi-label data sets show that Ml-rbf achieves highly competitive performance to other well-established multi-label learning algorithms.