Machine Learning
The nature of statistical learning theory
The nature of statistical learning theory
Making large-scale support vector machine learning practical
Advances in kernel methods
Fast training of support vector machines using sequential minimal optimization
Advances in kernel methods
MML clustering of multi-state, Poisson, vonMises circular and Gaussian distributions
Statistics and Computing
Minimum Message Length Grouping of Ordered Data
ALT '00 Proceedings of the 11th International Conference on Algorithmic Learning Theory
Training Support Vector Machines: an Application to Face Detection
CVPR '97 Proceedings of the 1997 Conference on Computer Vision and Pattern Recognition (CVPR '97)
A preliminary MML linear classifier using principal components for multiple classes
AI'05 Proceedings of the 18th Australian Joint conference on Advances in Artificial Intelligence
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This paper presents a preliminary attempt at performing extrinsic binary classification by reformulating the Support Vector Machine (SVM) approach in a Bayesian Message Length framework. The reformulation uses the Minimum Message Length (MML) principle as a way of costing each hyperplane via a two-part message. This message defines a separating hyperplane. The length of this message is used as an objective function for a search through the hypothesis space of possible hyperplanes used to dichotomise a set of data points.Two preliminary MML implementations are presented here, which differ in the (Bayesian) coding schemes used and the search procedures. The generalisation ability of these two reformulations on both artificial and real data sets are compared against current implementations of Support Vector Machines - namely SVMlight, the Lagrangian Support Vector Machine and SMOBR.It was found that, in general, all implementations improved as the size of the data sets increased. The MML implementations tended to perform best on the inseparable data sets and the real data set. Our preliminary MML scheme showed itself to be a strong competitor against the classical SVM, despite inefficiencies in the current scheme.