Automatic text processing
Hierarchical classification of Web content
SIGIR '00 Proceedings of the 23rd annual international ACM SIGIR conference on Research and development in information retrieval
An introduction to support Vector Machines: and other kernel-based learning methods
An introduction to support Vector Machines: and other kernel-based learning methods
Text Categorization with Suport Vector Machines: Learning with Many Relevant Features
ECML '98 Proceedings of the 10th European Conference on Machine Learning
Syllables and other String Kernel Extensions
ICML '02 Proceedings of the Nineteenth International Conference on Machine Learning
Hierarchically Classifying Documents Using Very Few Words
ICML '97 Proceedings of the Fourteenth International Conference on Machine Learning
Improving Text Classification by Shrinkage in a Hierarchy of Classes
ICML '98 Proceedings of the Fifteenth International Conference on Machine Learning
Text classification using string kernels
The Journal of Machine Learning Research
The Journal of Machine Learning Research
A survey of kernels for structured data
ACM SIGKDD Explorations Newsletter
Kernel Methods for Pattern Analysis
Kernel Methods for Pattern Analysis
RCV1: A New Benchmark Collection for Text Categorization Research
The Journal of Machine Learning Research
Kernel conditional random fields: representation and clique selection
ICML '04 Proceedings of the twenty-first international conference on Machine learning
Support vector machine learning for interdependent and structured output spaces
ICML '04 Proceedings of the twenty-first international conference on Machine learning
Large margin hierarchical classification
ICML '04 Proceedings of the twenty-first international conference on Machine learning
Learning associative Markov networks
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
Efficient Computation of Gapped Substring Kernels on Large Alphabets
The Journal of Machine Learning Research
The generalized distributive law
IEEE Transactions on Information Theory
Factor graphs and the sum-product algorithm
IEEE Transactions on Information Theory
Tree-based reparameterization framework for analysis of sum-product and related algorithms
IEEE Transactions on Information Theory
Hierarchical, perceptron-like learning for ontology-based information extraction
Proceedings of the 16th international conference on World Wide Web
The Interplay of Optimization and Machine Learning Research
The Journal of Machine Learning Research
Multilabel classification via calibrated label ranking
Machine Learning
Decision trees for hierarchical multi-label classification
Machine Learning
Large scale multi-label classification via metalabeler
Proceedings of the 18th international conference on World wide web
Preferential text classification: learning algorithms and evaluation measures
Information Retrieval
Semi-automatic dynamic auxiliary-tag-aided image annotation
Pattern Recognition
User Insisted Redistribution of Belief in Hierarchical Classification Spaces
WI-IAT '09 Proceedings of the 2009 IEEE/WIC/ACM International Joint Conference on Web Intelligence and Intelligent Agent Technology - Volume 02
Confusion and Distance Metrics as Performance Criteria for Hierarchical Classification Spaces
WI-IAT '09 Proceedings of the 2009 IEEE/WIC/ACM International Joint Conference on Web Intelligence and Intelligent Agent Technology - Volume 02
Hierarchical multi-class text categorization with global margin maximization
ACLShort '09 Proceedings of the ACL-IJCNLP 2009 Conference Short Papers
Does SVM really scale up to large bag of words feature spaces?
IDA'07 Proceedings of the 7th international conference on Intelligent data analysis
Probabilistic structured predictors
UAI '09 Proceedings of the Twenty-Fifth Conference on Uncertainty in Artificial Intelligence
FSKD'09 Proceedings of the 6th international conference on Fuzzy systems and knowledge discovery - Volume 1
A semi-dependent decomposition approach to learn hierarchical classifiers
Pattern Recognition
Mr.KNN: soft relevance for multi-label classification
CIKM '10 Proceedings of the 19th ACM international conference on Information and knowledge management
Structured output prediction of anti-cancer drug activity
PRIB'10 Proceedings of the 5th IAPR international conference on Pattern recognition in bioinformatics
Incorporating the loss function into discriminative clustering of structured outputs
IEEE Transactions on Neural Networks
Discovering missing values in semi-structured databases
Large Scale Semantic Access to Content (Text, Image, Video, and Sound)
A survey of hierarchical classification across different application domains
Data Mining and Knowledge Discovery
Hierarchical classification with dynamic-threshold SVM ensemble for gene function prediction
ADMA'10 Proceedings of the 6th international conference on Advanced data mining and applications - Volume Part II
On exploiting hierarchical label structure with pairwise classifiers
ACM SIGKDD Explorations Newsletter
Two-phase prediction of protein functions from biological literature based on Gini-Index
Proceedings of the 5th International Conference on Ubiquitous Information Management and Communication
Hierarchical text classification with latent concepts
HLT '11 Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies: short papers - Volume 2
Multi-task drug bioactivity classification with graph labeling ensembles
PRIB'11 Proceedings of the 6th IAPR international conference on Pattern recognition in bioinformatics
Large Margin Hierarchical Classification with Mutually Exclusive Class Membership
The Journal of Machine Learning Research
Learning data structure from classes: A case study applied to population genetics
Information Sciences: an International Journal
Exploiting label dependency for hierarchical multi-label classification
PAKDD'12 Proceedings of the 16th Pacific-Asia conference on Advances in Knowledge Discovery and Data Mining - Volume Part I
Modeling topic dependencies in hierarchical text categorization
ACL '12 Proceedings of the 50th Annual Meeting of the Association for Computational Linguistics: Long Papers - Volume 1
Tree ensembles for predicting structured outputs
Pattern Recognition
Multi-Label Classification Method for Multimedia Tagging
International Journal of Multimedia Data Engineering & Management
Learning to rank from structures in hierarchical text classification
ECIR'13 Proceedings of the 35th European conference on Advances in Information Retrieval
Incremental reranking for hierarchical text classification
ECIR'13 Proceedings of the 35th European conference on Advances in Information Retrieval
Recursive regularization for large-scale classification with hierarchical and graphical dependencies
Proceedings of the 19th ACM SIGKDD international conference on Knowledge discovery and data mining
Multi-label learning with millions of labels: recommending advertiser bid phrases for web pages
Proceedings of the 22nd international conference on World Wide Web
Multilabel relationship learning
ACM Transactions on Knowledge Discovery from Data (TKDD)
Intelligent Data Analysis
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We present a kernel-based algorithm for hierarchical text classification where the documents are allowed to belong to more than one category at a time. The classification model is a variant of the Maximum Margin Markov Network framework, where the classification hierarchy is represented as a Markov tree equipped with an exponential family defined on the edges. We present an efficient optimization algorithm based on incremental conditional gradient ascent in single-example subspaces spanned by the marginal dual variables. The optimization is facilitated with a dynamic programming based algorithm that computes best update directions in the feasible set. Experiments show that the algorithm can feasibly optimize training sets of thousands of examples and classification hierarchies consisting of hundreds of nodes. Training of the full hierarchical model is as efficient as training independent SVM-light classifiers for each node. The algorithm's predictive accuracy was found to be competitive with other recently introduced hierarchical multi-category or multilabel classification learning algorithms.