Bayesian Classification With Gaussian Processes
IEEE Transactions on Pattern Analysis and Machine Intelligence
Sparse on-line Gaussian processes
Neural Computation
Conditional Random Fields: Probabilistic Models for Segmenting and Labeling Sequence Data
ICML '01 Proceedings of the Eighteenth International Conference on Machine Learning
Sparse Greedy Matrix Approximation for Machine Learning
ICML '00 Proceedings of the Seventeenth International Conference on Machine Learning
Maximum Entropy Markov Models for Information Extraction and Segmentation
ICML '00 Proceedings of the Seventeenth International Conference on Machine Learning
MARK: a boosting algorithm for heterogeneous kernel models
Proceedings of the eighth ACM SIGKDD international conference on Knowledge discovery and data mining
An Equivalence Between Sparse Approximation and Support Vector Machines
An Equivalence Between Sparse Approximation and Support Vector Machines
A family of algorithms for approximate bayesian inference
A family of algorithms for approximate bayesian inference
On the algorithmic implementation of multiclass kernel-based vector machines
The Journal of Machine Learning Research
EMNLP '02 Proceedings of the ACL-02 conference on Empirical methods in natural language processing - Volume 10
Gaussian Processes for Classification: Mean-Field Algorithms
Neural Computation
Computing upper and lower bounds on likelihoods in intractable networks
UAI'96 Proceedings of the Twelfth international conference on Uncertainty in artificial intelligence
Variational Gaussian process classifiers
IEEE Transactions on Neural Networks
Learning as search optimization: approximate large margin methods for structured prediction
ICML '05 Proceedings of the 22nd international conference on Machine learning
Search-based structured prediction
Machine Learning
Classification of Protein Interaction Sentences via Gaussian Processes
PRIB '09 Proceedings of the 4th IAPR International Conference on Pattern Recognition in Bioinformatics
Discriminative temporal smoothing for activity recognition from wearable sensors
UCS'07 Proceedings of the 4th international conference on Ubiquitous computing systems
Protein interaction detection in sentences via Gaussian Processes: a preliminary evaluation
International Journal of Data Mining and Bioinformatics
Conditional graphical models for protein structure prediction
Conditional graphical models for protein structure prediction
A conditional model for tonal analysis
ISMIS'06 Proceedings of the 16th international conference on Foundations of Intelligent Systems
Multi-view discriminative sequential learning
ECML'05 Proceedings of the 16th European conference on Machine Learning
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Many real-world classification tasks involve the prediction of multiple, inter-dependent class labels. A prototypical case of this sort deals with prediction of a sequence of labels for a sequence of observations. Such problems arise naturally in the context of annotating and segmenting observation sequences. This paper generalizes Gaussian Process classification to predict multiple labels by taking dependencies between neighboring labels into account. Our approach is motivated by the desire to retain rigorous probabilistic semantics, while overcoming limitations of parametric methods like Conditional Random Fields, which exhibit conceptual and computational difficulties in high-dimensional input spaces. Experiments on named entity recognition and pitch accent prediction tasks demonstrate the competitiveness of our approach.