An introduction to support Vector Machines: and other kernel-based learning methods
An introduction to support Vector Machines: and other kernel-based learning methods
A Tutorial on Support Vector Machines for Pattern Recognition
Data Mining and Knowledge Discovery
Sparse Greedy Matrix Approximation for Machine Learning
ICML '00 Proceedings of the Seventeenth International Conference on Machine Learning
Improved heterogeneous distance functions
Journal of Artificial Intelligence Research
Circular backpropagation networks for classification
IEEE Transactions on Neural Networks
EURASIP Journal on Advances in Signal Processing
MM '09 Proceedings of the 17th ACM international conference on Multimedia
Color texture classification using rao distance between multivariate copula based models
CAIP'11 Proceedings of the 14th international conference on Computer analysis of images and patterns - Volume Part II
Dynamic similarity kernel for visual recognition
KES'06 Proceedings of the 10th international conference on Knowledge-Based Intelligent Information and Engineering Systems - Volume Part II
On the Geometry of Multivariate Generalized Gaussian Models
Journal of Mathematical Imaging and Vision
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When dealing with pattern recognition problems one encounters different types of prior knowledge. It is important to incorporate such knowledge into the classification method at hand. A common prior knowledge is that many datasets are on some kinds of manifolds. Distance-based classification methods can make use of this by a modified distance measure called geodesic distance. We introduce a new kind of kernels for a support vector machine (SVM) which incorporates geodesic distance and therefore is applicable in cases where such transformation invariance is known. Experiments results show that the performance of our method is comparable to that of other state-of-the-art methods, such as SVM-based Euclidean distance.