Introduction to statistical pattern recognition (2nd ed.)
Introduction to statistical pattern recognition (2nd ed.)
The nature of statistical learning theory
The nature of statistical learning theory
Hierarchical classification of Web content
SIGIR '00 Proceedings of the 23rd annual international ACM SIGIR conference on Research and development in information retrieval
Visualization and interactive feature selection for unsupervised data
Proceedings of the sixth ACM SIGKDD international conference on Knowledge discovery and data mining
Enlarging the Margins in Perceptron Decision Trees
Machine Learning
On the Learnability and Design of Output Codes for Multiclass Problems
COLT '00 Proceedings of the Thirteenth Annual Conference on Computational Learning Theory
IEEE Transactions on Knowledge and Data Engineering
Configurable Hybrid Architectures for Character Recognition Applications
ICDAR '05 Proceedings of the Eighth International Conference on Document Analysis and Recognition
Minimum spanning trees in hierarchical multiclass support vector machines generation
IEA/AIE'2005 Proceedings of the 18th international conference on Innovations in Applied Artificial Intelligence
Protein cellular localization prediction with Support Vector Machines and Decision Trees
Computers in Biology and Medicine
Margin Trees for High-dimensional Classification
The Journal of Machine Learning Research
Discriminative classifiers for deterministic dependency parsing
COLING-ACL '06 Proceedings of the COLING/ACL on Main conference poster sessions
Comparing Combination Rules of Pairwise Neural Networks Classifiers
Neural Processing Letters
Hierarchical learning strategy in semantic relation extraction
Information Processing and Management: an International Journal
Discovering relationships among categories using misclassification information
Proceedings of the 2008 ACM symposium on Applied computing
Robust and efficient multiclass SVM models for phrase pattern recognition
Pattern Recognition
Supplier selection based on hierarchical potential support vector machine
Expert Systems with Applications: An International Journal
ECML PKDD '09 Proceedings of the European Conference on Machine Learning and Knowledge Discovery in Databases: Part I
A maximum likelihood framework for integrating taxonomies
AAAI'05 Proceedings of the 20th national conference on Artificial intelligence - Volume 2
A review on the combination of binary classifiers in multiclass problems
Artificial Intelligence Review
A support vector hierarchical method for multi-class classification and rejection
IJCNN'09 Proceedings of the 2009 international joint conference on Neural Networks
Detecting relationships among categories using text classification
Journal of the American Society for Information Science and Technology
New clustering algorithms for the support vector machine based hierarchical classification
Pattern Recognition Letters
Large margin classifiers based on affine hulls
Neurocomputing
Disease classification from capillary electrophoresis: mass spectrometry
ICAPR'05 Proceedings of the Third international conference on Advances in Pattern Recognition - Volume Part I
Protein cellular localization with multiclass support vector machines and decision trees
BSB'05 Proceedings of the 2005 Brazilian conference on Advances in Bioinformatics and Computational Biology
Protein fold recognition with combined SVM-RDA classifier
HAIS'10 Proceedings of the 5th international conference on Hybrid Artificial Intelligence Systems - Volume Part I
Learning data structure from classes: A case study applied to population genetics
Information Sciences: an International Journal
Hyperdisk based large margin classifier
Pattern Recognition
Large scale visual classification with many classes
MLDM'13 Proceedings of the 9th international conference on Machine Learning and Data Mining in Pattern Recognition
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We introduce a framework, which we call Divide-by-2 (DB2), for extending support vector machines (SVM) to multi-class problems. DB2 offers an alternative to the standard one-against-one and one-against-rest algorithms. For an N class problem, DB2 produces an N − 1 node binary decision tree where nodes represent decision boundaries formed by N − 1 SVM binary classifiers. This tree structure allows us to present a generalization and a time complexity analysis of DB2. Our analysis and related experiments show that, DB2 is faster than one-against-one and one-against-rest algorithms in terms of testing time, significantly faster than one-against-rest in terms of training time, and that the cross-validation accuracy of DB2 is comparable to these two methods.