A training algorithm for optimal margin classifiers
COLT '92 Proceedings of the fifth annual workshop on Computational learning theory
Bayesian methods for adaptive models
Bayesian methods for adaptive models
Prediction of generalization ability in learning machines
Prediction of generalization ability in learning machines
The nature of statistical learning theory
The nature of statistical learning theory
Playing billiards in version space
Neural Computation
COLT' 98 Proceedings of the eleventh annual conference on Computational learning theory
Bayesian Classification With Gaussian Processes
IEEE Transactions on Pattern Analysis and Machine Intelligence
Making large-scale support vector machine learning practical
Advances in kernel methods
Fast training of support vector machines using sequential minimal optimization
Advances in kernel methods
COLT '99 Proceedings of the twelfth annual conference on Computational learning theory
Prediction with Gaussian processes: from linear regression to linear prediction and beyond
Learning in graphical models
An introduction to support Vector Machines: and other kernel-based learning methods
An introduction to support Vector Machines: and other kernel-based learning methods
Machine Learning
AI Game Programming Wisdom
Bayesian Learning for Neural Networks
Bayesian Learning for Neural Networks
Neural Networks for Pattern Recognition
Neural Networks for Pattern Recognition
Text Categorization with Suport Vector Machines: Learning with Many Relevant Features
ECML '98 Proceedings of the 10th European Conference on Machine Learning
ICML '00 Proceedings of the Seventeenth International Conference on Machine Learning
Sparsity vs. Large Margins for Linear Classifiers
COLT '00 Proceedings of the Thirteenth Annual Conference on Computational Learning Theory
Support Vector Machines: Training and Applications
Support Vector Machines: Training and Applications
Gaussian Processes for Classification: Mean-Field Algorithms
Neural Computation
Expectation propagation for approximate Bayesian inference
UAI'01 Proceedings of the Seventeenth conference on Uncertainty in artificial intelligence
Structural risk minimization over data-dependent hierarchies
IEEE Transactions on Information Theory
Input space versus feature space in kernel-based methods
IEEE Transactions on Neural Networks
Comparing Linear Discriminant Analysis and Support Vector Machines
ADVIS '02 Proceedings of the Second International Conference on Advances in Information Systems
Support vector machine active learning with applications to text classification
The Journal of Machine Learning Research
The Journal of Machine Learning Research
ICDM '03 Proceedings of the Third IEEE International Conference on Data Mining
A bootstrapping approach to annotating large image collection
MIR '03 Proceedings of the 5th ACM SIGMM international workshop on Multimedia information retrieval
Online Choice of Active Learning Algorithms
The Journal of Machine Learning Research
Retrieval of difficult image classes using svd-based relevance feedback
Proceedings of the 6th ACM SIGMM international workshop on Multimedia information retrieval
Adapting two-class support vector classification methods to many class problems
ICML '05 Proceedings of the 22nd international conference on Machine learning
Compact approximations to Bayesian predictive distributions
ICML '05 Proceedings of the 22nd international conference on Machine learning
Symbolic Signatures for Deformable Shapes
IEEE Transactions on Pattern Analysis and Machine Intelligence
Machine Learning
Multilingual dependency parsing using Bayes Point Machines
HLT-NAACL '06 Proceedings of the main conference on Human Language Technology Conference of the North American Chapter of the Association of Computational Linguistics
Automatic prediction of frustration
International Journal of Human-Computer Studies
Learning iteratively a classifier with the Bayesian Model Averaging Principle
Pattern Recognition
Comparison-based algorithms are robust and randomized algorithms are anytime
Evolutionary Computation
Bayes Machines for binary classification
Pattern Recognition Letters
Confidence-weighted linear classification
Proceedings of the 25th international conference on Machine learning
Sparse Bayes Machines for Binary Classification
ICANN '08 Proceedings of the 18th international conference on Artificial Neural Networks, Part I
Learning Similarity Functions from Qualitative Feedback
ECCBR '08 Proceedings of the 9th European conference on Advances in Case-Based Reasoning
A Uniform Lower Error Bound for Half-Space Learning
ALT '08 Proceedings of the 19th international conference on Algorithmic Learning Theory
Patch Learning for Incremental Classifier Design
Proceedings of the 2006 conference on ECAI 2006: 17th European Conference on Artificial Intelligence August 29 -- September 1, 2006, Riva del Garda, Italy
Optimal robust expensive optimization is tractable
Proceedings of the 11th Annual conference on Genetic and evolutionary computation
The impact of parse quality on syntactically-informed statistical machine translation
EMNLP '06 Proceedings of the 2006 Conference on Empirical Methods in Natural Language Processing
Preferential text classification: learning algorithms and evaluation measures
Information Retrieval
Boosting Active Learning to Optimality: A Tractable Monte-Carlo, Billiard-Based Algorithm
ECML PKDD '09 Proceedings of the European Conference on Machine Learning and Knowledge Discovery in Databases: Part II
Learning to Rank for Information Retrieval
Foundations and Trends in Information Retrieval
A framework for kernel-based multi-category classification
Journal of Artificial Intelligence Research
HPSG supertagging: a sequence labeling view
IWPT '09 Proceedings of the 11th International Conference on Parsing Technologies
Maximum Relative Margin and Data-Dependent Regularization
The Journal of Machine Learning Research
PAKDD'03 Proceedings of the 7th Pacific-Asia conference on Advances in knowledge discovery and data mining
Network-based sparse Bayesian classification
Pattern Recognition
Using Gaussian process based kernel classifiers for credit rating forecasting
Expert Systems with Applications: An International Journal
Efficient learning of pseudo-boolean functions from limited training data
ISMIS'05 Proceedings of the 15th international conference on Foundations of Intelligent Systems
Forecasting foreign exchange rates using kernel methods
Expert Systems with Applications: An International Journal
Non-sparse multiple kernel fisher discriminant analysis
The Journal of Machine Learning Research
Confidence-weighted linear classification for text categorization
The Journal of Machine Learning Research
APRIL: active preference learning-based reinforcement learning
ECML PKDD'12 Proceedings of the 2012 European conference on Machine Learning and Knowledge Discovery in Databases - Volume Part II
Combining active learning and semi-supervised learning to construct SVM classifier
Knowledge-Based Systems
Adaptive regularization of weight vectors
Machine Learning
Texture classification of landsat TM imagery using Bayes point machine
Proceedings of the 51st ACM Southeast Conference
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Kernel-classifiers comprise a powerful class of non-linear decision functions for binary classification. The support vector machine is an example of a learning algorithm for kernel classifiers that singles out the consistent classifier with the largest margin, i.e. minimal real-valued output on the training sample, within the set of consistent hypotheses, the so-called version space. We suggest the Bayes point machine as a well-founded improvement which approximates the Bayes-optimal decision by the centre of mass of version space. We present two algorithms to stochastically approximate the centre of mass of version space: a billiard sampling algorithm and a sampling algorithm based on the well known perceptron algorithm. It is shown how both algorithms can be extended to allow for soft-boundaries in order to admit training errors. Experimentally, we find that - for the zero training error case - Bayes point machines consistently outperform support vector machines on both surrogate data and real-world benchmark data sets. In the soft-boundary/soft-margin case, the improvement over support vector machines is shown to be reduced. Finally, we demonstrate that the real-valued output of single Bayes points on novel test points is a valid confidence measure and leads to a steady decrease in generalisation error when used as a rejection criterion.