Probabilistic reasoning in intelligent systems: networks of plausible inference
Probabilistic reasoning in intelligent systems: networks of plausible inference
Bayesian Belief Networks as a tool for stochastic parsing
Speech Communication
Probabilistic independence networks for hidden Markov probability models
Neural Computation
Introduction to Bayesian Networks
Introduction to Bayesian Networks
Bayesian Networks for Data Mining
Data Mining and Knowledge Discovery
Modeling Documents for Structure Recognition Using Generalized N-Grams
ICDAR '97 Proceedings of the 4th International Conference on Document Analysis and Recognition
Clustering and classification of document structure-a machine learning approach
ICDAR '95 Proceedings of the Third International Conference on Document Analysis and Recognition (Volume 2) - Volume 2
Structured Document Segmentation and Representation by the Modified X-Y tree
ICDAR '99 Proceedings of the Fifth International Conference on Document Analysis and Recognition
A general framework for adaptive processing of data structures
IEEE Transactions on Neural Networks
ACM SIGKDD Explorations Newsletter
Managing and analyzing carbohydrate data
ACM SIGMOD Record
IEEE Transactions on Knowledge and Data Engineering
Recursive self-organizing network models
Neural Networks - 2004 Special issue: New developments in self-organizing systems
Universal Approximation Capability of Cascade Correlation for Structures
Neural Computation
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)
ICML '09 Proceedings of the 26th Annual International Conference on Machine Learning
Modelling Stem Cells Lineages with Markov Trees
PRIB '09 Proceedings of the 4th IAPR International Conference on Pattern Recognition in Bioinformatics
Picture extraction from digitized historical manuscripts
Proceedings of the ACM International Conference on Image and Video Retrieval
Hidden Markov tree model in dependency-based machine translation
ACLShort '09 Proceedings of the ACL-IJCNLP 2009 Conference Short Papers
A novel rotationally invariant region-based hidden Markov model for efficient 3-D image segmentation
IEEE Transactions on Image Processing - Special section on distributed camera networks: sensing, processing, communication, and implementation
Contourlet filter design based on chebyshev best uniform approximation
EURASIP Journal on Advances in Signal Processing
Bottom-up generative modeling of tree-structured data
ICONIP'10 Proceedings of the 17th international conference on Neural information processing: theory and algorithms - Volume Part I
Automatic segmentation of digitalized historical manuscripts
Multimedia Tools and Applications
Neurocomputing
A generative multiset kernel for structured data
ICANN'12 Proceedings of the 22nd international conference on Artificial Neural Networks and Machine Learning - Volume Part I
An input-output hidden Markov model for tree transductions
Neurocomputing
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Classification is an important problem in image document processing and is often a preliminary step toward recognition, understanding, and information extraction. In this paper, the problem is formulated in the framework of concept learning and each category corresponds to the set of image documents with similar physical structure. We propose a solution based on two algorithmic ideas. First, we obtain a structured representation of images based on labeled XY-trees (this representation informs the learner about important relationships between image subconstituents). Second, we propose a probabilistic architecture that extends hidden Markov models for learning probability distributions defined on spaces of labeled trees. Finally, a successful application of this method to the categorization of commercial invoices is presented.