Making large-scale support vector machine learning practical
Advances in kernel methods
Pairwise classification and support vector machines
Advances in kernel methods
Support vector machines: hype or hallelujah?
ACM SIGKDD Explorations Newsletter - Special issue on “Scalable data mining algorithms”
Learning to Classify Text Using Support Vector Machines: Methods, Theory and Algorithms
Learning to Classify Text Using Support Vector Machines: Methods, Theory and Algorithms
Text Categorization with Suport Vector Machines: Learning with Many Relevant Features
ECML '98 Proceedings of the 10th European Conference on Machine Learning
Support Vector Machines in Relational Databases
SVM '02 Proceedings of the First International Workshop on Pattern Recognition with Support Vector Machines
On the algorithmic implementation of multiclass kernel-based vector machines
The Journal of Machine Learning Research
Support vector machine learning for interdependent and structured output spaces
ICML '04 Proceedings of the twenty-first international conference on Machine learning
Introduction to Machine Learning (Adaptive Computation and Machine Learning)
Introduction to Machine Learning (Adaptive Computation and Machine Learning)
Support Vector Machines for Pattern Classification (Advances in Pattern Recognition)
Support Vector Machines for Pattern Classification (Advances in Pattern Recognition)
Biomedical named entity recognition using two-phase model based on SVMs
Journal of Biomedical Informatics - Special issue: Named entity recognition in biomedicine
SVM in oracle database 10g: removing the barriers to widespread adoption of support vector machines
VLDB '05 Proceedings of the 31st international conference on Very large data bases
Large Margin Methods for Structured and Interdependent Output Variables
The Journal of Machine Learning Research
A support vector method for multivariate performance measures
ICML '05 Proceedings of the 22nd international conference on Machine learning
Training linear SVMs in linear time
Proceedings of the 12th ACM SIGKDD international conference on Knowledge discovery and data mining
Recognizing names in biomedical texts using hidden Markov model and SVM plus sigmoid
JNLPBA '04 Proceedings of the International Joint Workshop on Natural Language Processing in Biomedicine and its Applications
Introduction to the bio-entity recognition task at JNLPBA
JNLPBA '04 Proceedings of the International Joint Workshop on Natural Language Processing in Biomedicine and its Applications
Incorporating lexical knowledge into biomedical NE recognition
JNLPBA '04 Proceedings of the International Joint Workshop on Natural Language Processing in Biomedicine and its Applications
Adapting an NER-system for German to the biomedical domain
JNLPBA '04 Proceedings of the International Joint Workshop on Natural Language Processing in Biomedicine and its Applications
Exploring deep knowledge resources in biomedical name recognition
JNLPBA '04 Proceedings of the International Joint Workshop on Natural Language Processing in Biomedicine and its Applications
POSBIOTM-NER in the shared task of BioNLP/NLPBA 2004
JNLPBA '04 Proceedings of the International Joint Workshop on Natural Language Processing in Biomedicine and its Applications
A comparison of methods for multiclass support vector machines
IEEE Transactions on Neural Networks
Towards a Protein-Protein Interaction information extraction system: Recognizing named entities
Knowledge-Based Systems
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This paper explores scalability issues associated with the Named Entity Recognition problem in the biomedical publications domain using Support Vector Machines. The performance results using existing binary and multi-class SVMs with increasing training data are compared to results obtained using our new implementations. Our approach eliminates prior language or domain-specific knowledge and achieves good out-of-the-box accuracy measures comparable to those obtained using more complex approaches. The training time of multi-class SVMs is reduced by several orders of magnitude, which would make support vector machines a more viable and practical solution for real-world problems with large datasets.