A training algorithm for optimal margin classifiers
COLT '92 Proceedings of the fifth annual workshop on Computational learning theory
Fast training of support vector machines using sequential minimal optimization
Advances in kernel methods
Learning with Kernels: Support Vector Machines, Regularization, Optimization, and Beyond
Learning with Kernels: Support Vector Machines, Regularization, Optimization, and Beyond
A generalized kernel approach to dissimilarity-based classification
The Journal of Machine Learning Research
Pattern Recognition and Machine Learning (Information Science and Statistics)
Pattern Recognition and Machine Learning (Information Science and Statistics)
Prototype selection for dissimilarity-based classifiers
Pattern Recognition
A theory of learning with similarity functions
Machine Learning
IEEE Transactions on Visualization and Computer Graphics
Similarity-based Classification: Concepts and Algorithms
The Journal of Machine Learning Research
IEEE Transactions on Knowledge and Data Engineering
Brain Decoding: Biases in Error Estimation
WBD '10 Proceedings of the 2010 First Workshop on Brain Decoding: Pattern Recognition Challenges in Neuroimaging
Automated atlas-based clustering of white matter fiber tracts from DTMRI
MICCAI'05 Proceedings of the 8th international conference on Medical Image Computing and Computer-Assisted Intervention - Volume Part I
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In this work we study the problem of supervised tract segmentation from tractography data, a vectorial representation of the brain connectivity extracted from diffusion magnetic resonance images. We report a case study based on a dataset where for each tractography of three subjects the segmentation of eight major anatomical tracts was manually operated by expert neuroanatomists. Domain specific distances that encodes the dissimilarity of tracts do not allow to define a positive semi-definite kernel function.We show that a dissimilarity representation based on such distances enables the successful design of a classifier. This approach provides a robust encoding which proves to be effective using a linear classifier. Our empirical analysis shows that we obtain better tract segmentation than previously proposed methods.