The nature of statistical learning theory
The nature of statistical learning theory
Selection of relevant features and examples in machine learning
Artificial Intelligence - Special issue on relevance
An algorithm for suffix stripping
Readings in information retrieval
Fast training of support vector machines using sequential minimal optimization
Advances in kernel methods
Combining support vector and mathematical programming methods for classification
Advances in kernel methods
Classification by pairwise coupling
NIPS '97 Proceedings of the 1997 conference on Advances in neural information processing systems 10
Hierarchical classification of Web content
SIGIR '00 Proceedings of the 23rd annual international ACM SIGIR conference on Research and development in information retrieval
Normalized Cuts and Image Segmentation
IEEE Transactions on Pattern Analysis and Machine Intelligence
Co-clustering documents and words using bipartite spectral graph partitioning
Proceedings of the seventh ACM SIGKDD international conference on Knowledge discovery and data mining
Machine learning in automated text categorization
ACM Computing Surveys (CSUR)
Analysis of Symbolic Data: Exploratory Methods for Extracting Statistical Information from Complex Data
On Issues of Instance Selection
Data Mining and Knowledge Discovery
Hierarchical Text Categorization Using Neural Networks
Information Retrieval
Text Categorization with Suport Vector Machines: Learning with Many Relevant Features
ECML '98 Proceedings of the 10th European Conference on Machine Learning
Reliable Classifications with Machine Learning
ECML '02 Proceedings of the 13th European Conference on Machine Learning
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
Transductive Inference for Text Classification using Support Vector Machines
ICML '99 Proceedings of the Sixteenth International Conference on Machine Learning
Learning with progressive transductive support vector machine
Pattern Recognition Letters
A study of distance-based machine learning algorithms
A study of distance-based machine learning algorithms
Classifying web documents in a hierarchy of categories: a comprehensive study
Journal of Intelligent Information Systems
Hierarchical Text Categorization in a Transductive Setting
ICDMW '08 Proceedings of the 2008 IEEE International Conference on Data Mining Workshops
UAI'98 Proceedings of the Fourteenth conference on Uncertainty in artificial intelligence
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In many textual repositories, documents are organized in a hierarchy of categories to support a thematic search by browsing topics of interests. In this paper we present a novel approach for automatic classification of documents into a hierarchy of categories that works in the transductive setting and exploits relevant example selection. While resorting to the transductive learning setting permits to classify repositories where only few examples are labelled by exploiting information potentially conveyed by unlabelled data, relevant example selection permits to tame the complexity of the task and increase the rate of learning by focusing only on informative examples. Results on real world datasets show the effectiveness of the proposed solutions.