A training algorithm for optimal margin classifiers
COLT '92 Proceedings of the fifth annual workshop on Computational learning theory
The nature of statistical learning theory
The nature of statistical learning theory
A maximum entropy approach to natural language processing
Computational Linguistics
Making large-scale support vector machine learning practical
Advances in kernel methods
Foundations of statistical natural language processing
Foundations of statistical natural language processing
Information Retrieval
Bayesian online classifiers for text classification and filtering
SIGIR '02 Proceedings of the 25th annual international ACM SIGIR conference on Research and development in information retrieval
Naive (Bayes) at Forty: The Independence Assumption in Information Retrieval
ECML '98 Proceedings of the 10th European Conference on Machine Learning
Text Categorization with Suport Vector Machines: Learning with Many Relevant Features
ECML '98 Proceedings of the 10th European Conference on Machine Learning
On the algorithmic implementation of multiclass kernel-based vector machines
The Journal of Machine Learning Research
Comparing Naive Bayes, Decision Trees, and SVM with AUC and Accuracy
ICDM '03 Proceedings of the Third IEEE International Conference on Data Mining
Kernel Methods for Pattern Analysis
Kernel Methods for Pattern Analysis
Gaussian process classification for segmenting and annotating sequences
ICML '04 Proceedings of the twenty-first international conference on Machine learning
Biomedical named entity recognition using two-phase model based on SVMs
Journal of Biomedical Informatics - Special issue: Named entity recognition in biomedicine
Gaussian Processes for Ordinal Regression
The Journal of Machine Learning Research
Preference learning with Gaussian processes
ICML '05 Proceedings of the 22nd international conference on Machine learning
Two-phase biomedical NE recognition based on SVMs
BioMed '03 Proceedings of the ACL 2003 workshop on Natural language processing in biomedicine - Volume 13
Gaussian Processes for Machine Learning (Adaptive Computation and Machine Learning)
Gaussian Processes for Machine Learning (Adaptive Computation and Machine Learning)
Using SVM to Extract Acronyms from Text
Soft Computing - A Fusion of Foundations, Methodologies and Applications
On Text-based Mining with Active Learning and Background Knowledge Using SVM
Soft Computing - A Fusion of Foundations, Methodologies and Applications
Multi-way relation classification: application to protein-protein interactions
HLT '05 Proceedings of the conference on Human Language Technology and Empirical Methods in Natural Language Processing
Building Support Vector Machines with Reduced Classifier Complexity
The Journal of Machine Learning Research
Relaxed online SVMs for spam filtering
SIGIR '07 Proceedings of the 30th annual international ACM SIGIR conference on Research and development in information retrieval
Bioinformatics
Kernel design for RNA classification using Support Vector Machines
International Journal of Data Mining and Bioinformatics
Probabilistic multi-class multi-kernel learning
Bioinformatics
Comparative experiments on learning information extractors for proteins and their interactions
Artificial Intelligence in Medicine
Bio-medical entity extraction using support vector machines
Artificial Intelligence in Medicine
Automatic extraction of hierarchical relations from text
ESWC'06 Proceedings of the 3rd European conference on The Semantic Web: research and applications
Extensions of the informative vector machine
Proceedings of the First international conference on Deterministic and Statistical Methods in Machine Learning
ICCS'05 Proceedings of the 5th international conference on Computational Science - Volume Part II
Transactions on Computational Systems Biology II
A comparison of methods for multiclass support vector machines
IEEE Transactions on Neural Networks
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The non-parametric deterministic Support Vector Machines (SVMs) produce high levels of performances in text classification. This article offers a much needed evaluation of the Gaussian Process (GP) classifier, as a non-parametric probabilistic analogue to SVMs, which has been rarely applied to text classification. We provide an extensive experimental comparison of the performance and properties of these competing classifiers on the challenging problem of protein interaction detection in biomedical publications. Our results show that GPs can match the performance of SVMs without the need for costly margin parameter tuning, whilst offering the advantage of an extendable probabilistic framework for text classification.