Probabilistic reasoning in intelligent systems: networks of plausible inference
Probabilistic reasoning in intelligent systems: networks of plausible inference
Small Sample Size Effects in Statistical Pattern Recognition: Recommendations for Practitioners
IEEE Transactions on Pattern Analysis and Machine Intelligence
The Topology of Locales and Its Effects on Position Uncertainty
IEEE Transactions on Pattern Analysis and Machine Intelligence
Spatial Sampling of Printed Patterns
IEEE Transactions on Pattern Analysis and Machine Intelligence
Graphical models for machine learning and digital communication
Graphical models for machine learning and digital communication
Causality: models, reasoning, and inference
Causality: models, reasoning, and inference
An Introduction to Variational Methods for Graphical Models
Machine Learning
Comparing formal theories of context in AI
Artificial Intelligence
Style Context with Second-Order Statistics
IEEE Transactions on Pattern Analysis and Machine Intelligence
Style Consistent Classification of Isogenous Patterns
IEEE Transactions on Pattern Analysis and Machine Intelligence
IJCAI'93 Proceedings of the 13th international joint conference on Artifical intelligence - Volume 1
The generalized distributive law
IEEE Transactions on Information Theory
Analytical Results on Style-Constrained Bayesian Classification of Pattern Fields
IEEE Transactions on Pattern Analysis and Machine Intelligence
User profiling with hierarchical context: an e-Retailer case study
CONTEXT'07 Proceedings of the 6th international and interdisciplinary conference on Modeling and using context
Interactive, mobile, distributed pattern recognition
ICIAP'05 Proceedings of the 13th international conference on Image Analysis and Processing
Estimation, learning, and adaptation: systems that improve with use
SSPR'12/SPR'12 Proceedings of the 2012 Joint IAPR international conference on Structural, Syntactic, and Statistical Pattern Recognition
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Theories of context in logic enable reasoning and deduction in contexts represented as formal objects. Such theories are not readily applicable to systems that learn by induction from a set of examples. Probabilistic graphical models already provide the tools to exploit context represented as statistical dependences, thereby providing a unified methodology to incorporate context information in learning and inference. Drawing on a case study from optical character recognition, we present the various types of dependences that can occur in pattern classification problems and how such dependences can be exploited to increase classification accuracy. Learning under different conditions require differing amounts and kinds of samples and different trade-offs between modeling error due to overly strict independence assumptions and estimation error of models that are too elaborate for the size of the available training set. With a series of examples based on frames of two patterns we show how each kind of dependence can be represented using graphical models and present examples from other disciplines where the particular dependence frequently occurs.