Multi-class support vector machines

TL;DR: Probability distributions of linear models for regression and classification are given in this article, along with a discussion of combining models and combining models in the context of machine learning and classification.

TL;DR: Decomposition implementations for two "all-together" multiclass SVM methods are given and it is shown that for large problems methods by considering all data at once in general need fewer support vectors.

TL;DR: Support vector machines for dynamic reconstruction of a chaotic system, Klaus-Robert Muller et al pairwise classification and support vector machines, Ulrich Kressel.

TL;DR: This issue's collection of essays should help familiarize readers with this interesting new racehorse in the Machine Learning stable, and give a practical guide and a new technique for implementing the algorithm efficiently.

TL;DR: Presenting a method for determining the necessary and sufficient conditions for consistency of learning process, the author covers function estimates from small data pools, applying these estimations to real-life problems, and much more.

TL;DR: It is observed that three different types of handwritten digit classifiers construct their decision surface from strongly overlapping small subsets of the data base, which opens up the possibility of compressing data bases significantly by disposing of theData which is not important for the solution of a given task.

TL;DR: Two view-based object recognition algorithms are compared: a heuristic algorithm based on oriented filters, and a support vector learning machine trained on low-resolution images of the objects.