Exploiting generative models in discriminative classifiers
Proceedings of the 1998 conference on Advances in neural information processing systems II
Kernels for Semi-Structured Data
ICML '02 Proceedings of the Nineteenth International Conference on Machine Learning
Hidden Tree Markov Models for Document Image Classification
IEEE Transactions on Pattern Analysis and Machine Intelligence
ACL '02 Proceedings of the 40th Annual Meeting on Association for Computational Linguistics
Structure and semantics for expressive text kernels
Proceedings of the sixteenth ACM conference on Conference on information and knowledge management
A generalization of Haussler's convolution kernel: mapping kernel
Proceedings of the 25th international conference on Machine learning
Efficient convolution kernels for dependency and constituent syntactic trees
ECML'06 Proceedings of the 17th European conference on Machine Learning
IEEE Computational Intelligence Magazine
A new tree kernel based on SOM-SD
ICANN'10 Proceedings of the 20th international conference on Artificial neural networks: Part II
Measuring tree similarity for natural language processing based information retrieval
NLDB'10 Proceedings of the Natural language processing and information systems, and 15th international conference on Applications of natural language to information systems
A subpath kernel for rooted unordered trees
PAKDD'11 Proceedings of the 15th Pacific-Asia conference on Advances in knowledge discovery and data mining - Volume Part I
Extending tree kernels with topological information
ICANN'11 Proceedings of the 21th international conference on Artificial neural networks - Volume Part I
Neurocomputing
X-Class: Associative Classification of XML Documents by Structure
ACM Transactions on Information Systems (TOIS)
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Almost all tree kernels proposed in the literature match substructures without taking into account their relative positioning with respect to one another. In this paper, we propose a novel family of kernels which explicitly focus on this type of information. Specifically, after defining a family of tree kernels based on routes between nodes, we present an efficient implementation for a member of this family. Experimental results on four different datasets show that our method is able to reach state of the art performances, obtaining in some cases performances better than computationally more demanding tree kernels.