Probabilistic reasoning in intelligent systems: networks of plausible inference
Probabilistic reasoning in intelligent systems: networks of plausible inference
Fundamentals of speech recognition
Fundamentals of speech recognition
Predicting Protein Secondary Structure Using Stochastic Tree Grammars
Machine Learning - Special issue on learning with probabilistic representations
Data on the Web: from relations to semistructured data and XML
Data on the Web: from relations to semistructured data and XML
The Hierarchical Hidden Markov Model: Analysis and Applications
Machine Learning
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
A graphical environment for change detection in structured documents
COMPSAC '97 Proceedings of the 21st International Computer Software and Applications Conference
Simplified Training Algorithms for Hierarchical Hidden Markov Models
DS '01 Proceedings of the 4th International Conference on Discovery Science
Hidden Tree Markov Models for Document Image Classification
IEEE Transactions on Pattern Analysis and Machine Intelligence
Online Algorithms for Mining Semi-structured Data Stream
ICDM '02 Proceedings of the 2002 IEEE International Conference on Data Mining
TreeFinder: a First Step towards XML Data Mining
ICDM '02 Proceedings of the 2002 IEEE International Conference on Data Mining
Model-Based Clustering and Visualization of Navigation Patterns on a Web Site
Data Mining and Knowledge Discovery
XRules: an effective structural classifier for XML data
Proceedings of the ninth ACM SIGKDD international conference on Knowledge discovery and data mining
Cyclic pattern kernels for predictive graph mining
Proceedings of the tenth ACM SIGKDD international conference on Knowledge discovery and data mining
Extensions of marginalized graph kernels
ICML '04 Proceedings of the twenty-first international conference on Machine learning
Managing and analyzing carbohydrate data
ACM SIGMOD Record
Wavelet-based statistical signal processing using hidden Markovmodels
IEEE Transactions on Signal Processing
A new efficient probabilistic model for mining labeled ordered trees
Proceedings of the 12th ACM SIGKDD international conference on Knowledge discovery and data mining
A new efficient probabilistic model for mining labeled ordered trees applied to glycobiology
ACM Transactions on Knowledge Discovery from Data (TKDD)
ISAAC'06 Proceedings of the 17th international conference on Algorithms and Computation
PRIB'12 Proceedings of the 7th IAPR international conference on Pattern Recognition in Bioinformatics
Coloring based approach for matching unrooted and/or unordered trees
Pattern Recognition Letters
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Glycans, or carbohydrate sugar chains, which play a number of important roles in the development and functioning of multicellular organisms, can be regarded as labeled ordered trees. A recent increase in the documentation of glycan structures, especially in the form of database curation, has made mining glycans important for the understanding of living cells. We propose a probabilistic model for mining labeled ordered trees, and we further present an efficient learning algorithm for this model, based on an EM algorithm. The time and space complexities of this algorithm are rather favorable, falling within the practical limits set by a variety of existing probabilistic models, including stochastic context-free grammars. Experimental results have shown that, in a supervised problem setting, the proposed method outperformed five other competing methods by a statistically significant factor in all cases. We further applied the proposed method to aligning multiple glycan trees, and we detected biologically significant common subtrees in these alignments where the trees are automatically classified into subtypes already known in glycobiology. Extended abstracts of parts of the work presented in this paper have appeared in [35], [4], and [3].