Practical methods of optimization; (2nd ed.)
Practical methods of optimization; (2nd ed.)
Bayesian Classification With Gaussian Processes
IEEE Transactions on Pattern Analysis and Machine Intelligence
Prediction with Gaussian processes: from linear regression to linear prediction and beyond
Learning in graphical models
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
Efficient svm training using low-rank kernel representations
The Journal of Machine Learning Research
Estimators for stochastic "Unification-Based" grammars
ACL '99 Proceedings of the 37th annual meeting of the Association for Computational Linguistics on Computational Linguistics
Shallow parsing with conditional random fields
NAACL '03 Proceedings of the 2003 Conference of the North American Chapter of the Association for Computational Linguistics on Human Language Technology - Volume 1
Using conditional random fields to predict pitch accents in conversational speech
ACL '04 Proceedings of the 42nd Annual Meeting on Association for Computational Linguistics
Nomograms for visualizing support vector machines
Proceedings of the eleventh ACM SIGKDD international conference on Knowledge discovery in data mining
Heteroscedastic Gaussian process regression
ICML '05 Proceedings of the 22nd international conference on Machine learning
Step Size Adaptation in Reproducing Kernel Hilbert Space
The Journal of Machine Learning Research
Conditional random fields for multi-agent reinforcement learning
Proceedings of the 24th international conference on Machine learning
A reproducing kernel Hilbert space framework for pairwise time series distances
Proceedings of the 25th international conference on Machine learning
A Hilbert Space Embedding for Distributions
ALT '07 Proceedings of the 18th international conference on Algorithmic Learning Theory
Hilbert space embeddings of conditional distributions with applications to dynamical systems
ICML '09 Proceedings of the 26th Annual International Conference on Machine Learning
A Generalization of Forward-Backward Algorithm
ECML PKDD '09 Proceedings of the European Conference on Machine Learning and Knowledge Discovery in Databases: Part I
Unifying divergence minimization and statistical inference via convex duality
COLT'06 Proceedings of the 19th annual conference on Learning Theory
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In this paper we define conditional random fields in reproducing kernel Hilbert spaces and show connections to Gaussian Process classification. More specifically, we prove decomposition results for undirected graphical models and we give constructions for kernels. Finally we present efficient means of solving the optimization problem using reduced rank decompositions and we show how stationarity can be exploited efficiently in the optimization process.