NIPS 1998 Workshop
Large Margin Classifiers
Many pattern classifiers are represented as thresholded real-valued functions, eg: sigmoid neural networks, support vector machines, voting classifiers, and Bayesian schemes. There is currently a great deal of interest in algorithms that produce classifiers of this kind with large margins, where the margin is the amount by which the classifier's prediction is to the correct side of threshold. Recent theoretical and experimental results show that many learning algorithms (such as back-propagation, SVM methods, AdaBoost, and bagging) frequently produce classifiers with large margins, and that this leads to better generalization performance. Hence there is sufficient reason to believe that Large Margin Classifiers will become a core method of the standard machine learning toolbox.
The aims of this workshop are:
Special interest will be paid to the following issues
Date and Location
Due to the large interest the workshop will be held on two days, namely Friday and Saturday, December 4-5 1998 in Breckenridge, CO.
Getting in Touch
If you are interested in contributing please fill out the survey form, send e-mail to [email protected] or fax to +49-30-6392-1805 (Alex Smola)
Click here for the guidelines for authors regarding formatting and style of the contributions.