A training algorithm for optimal margin classifiers
COLT '92 Proceedings of the fifth annual workshop on Computational learning theory
Machine Learning
A design principles of a weighted finite-state transducer library
Theoretical Computer Science - Special issue on implementing automata
BoosTexter: A Boosting-based Systemfor Text Categorization
Machine Learning - Special issue on information retrieval
Path kernels and multiplicative updates
The Journal of Machine Learning Research
A general weighted grammar library
CIAA'04 Proceedings of the 9th international conference on Implementation and Application of Automata
Sequence kernels for predicting protein essentiality
Proceedings of the 25th international conference on Machine learning
Learning with Weighted Transducers
Proceedings of the 2009 conference on Finite-State Methods and Natural Language Processing: Post-proceedings of the 7th International Workshop FSMNLP 2008
Learning languages with rational kernels
COLT'07 Proceedings of the 20th annual conference on Learning theory
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Many machine learning problems in natural language processing, transaction-log analysis, or computational biology, require the analysis of variable-length sequences, or, more generally, distributions of variable-length sequences.Kernel methods introduced for fixed-size vectors have proven very successful in a variety of machine learning tasks. We recently introduced a new and general kernel framework, rational kernels, to extend these methods to the analysis of variable-length sequences or more generally distributions given by weighted automata. These kernels are efficient to compute and have been successfully used in applications such as spoken-dialog classification with Support Vector Machines.However, the rational kernels previously introduced in these applications do not fully encompass distributions over alternate sequences. They are based only on the counts of co-occurring subsequences averaged over the alternate paths without taking into accounts information about the higher-order moments of the distributions of these counts.In this paper, we introduce a new family of rational kernels, moment kernels, that precisely exploits this additional information. These kernels are distribution kernels based on moments of counts of strings. We describe efficient algorithms to compute moment kernels and apply them to several difficult spoken-dialog classification tasks. Our experiments show that using the second moment of the counts of n-gram sequences consistently improves the classification accuracy in these tasks.