LeRec: a NN/HMM hybrid for on-line handwriting recognition
Neural Computation
Bayesian Classification With Gaussian Processes
IEEE Transactions on Pattern Analysis and Machine Intelligence
Signal Processing
Selecting sub-set autoregressions from outlier contaminated data
Computational Statistics & Data Analysis
Nonparametric regression using linear combinations of basis functions
Statistics and Computing
Machine Printed Text and Handwriting Identification in Noisy Document Images
IEEE Transactions on Pattern Analysis and Machine Intelligence
Optimizing time-frequency kernels for classification
IEEE Transactions on Signal Processing
An overview of character recognition focused on off-line handwriting
IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews
Robust polynomial classifier using L1-norm minimization
Applied Intelligence
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The automatic classification of images is now widely used in a range of applications. These include the diagnosis of arthritis from joint images, the classification of environmental noise from spectrograms and automatic text analysis. However, satisfactory performance is difficult to achieve in uncontrolled environments, as images are often contaminated by high levels of noise, outliers and global contamination due to illumination changes and environmental effects. We address these issues using a semi-parametric modelling strategy and a novel robust Bayesian classifier. This model is driven by additive Gaussian noise with non-uniform variance to describe outliers and uses the parametric and non-parametric components to describe contamination of different types. We assess the performance of our approach in two experiments based on real and simulated data. These show that our approach can significantly outperform a number of competitors in uncontrolled environments.