A maximum entropy approach to natural language processing
Computational Linguistics
Prior knowledge in support vector kernels
NIPS '97 Proceedings of the 1997 conference on Advances in neural information processing systems 10
Statistical Image Object Recognition using Mixture Densities
Journal of Mathematical Imaging and Vision
Efficient Pattern Recognition Using a New Transformation Distance
Advances in Neural Information Processing Systems 5, [NIPS Conference]
Transformation Invariance in Pattern Recognition-Tangent Distance and Tangent Propagation
Neural Networks: Tricks of the Trade, this book is an outgrowth of a 1996 NIPS workshop
Discriminative Training of Gaussian Mixtures for Image Object Recognition
Mustererkennung 1999, 21. DAGM-Symposium
The Minimum Error Minimax Probability Machine
The Journal of Machine Learning Research
Deformation-Aware Log-Linear Models
Proceedings of the 31st DAGM Symposium on Pattern Recognition
Toward automated generation of parametric BIMs based on hybrid video and laser scanning data
Advanced Engineering Informatics
Improving a discriminative approach to object recognition using image patches
PR'05 Proceedings of the 27th DAGM conference on Pattern Recognition
Image retrieval and annotation using maximum entropy
CLEF'06 Proceedings of the 7th international conference on Cross-Language Evaluation Forum: evaluation of multilingual and multi-modal information retrieval
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The principle of maximum entropy is a powerful framework that can be used to estimate class posterior probabilities for pattern recognition tasks. In this paper, we show how this principle is related to the discriminative training of Gaussian mixture densities using the maximum mutual information criterion. This leads to a relaxation of the constraints on the covariance matrices to be positive (semi-) definite. Thus, we arrive at a conceptually simple model that allows to estimate a large number of free parameters reliably. We compare the proposed method with other state-of-the-art approaches in experiments with the well known US Postal Service handwritten digits recognition task.