相关论文: Rejoinder to "Support Vector Machines with Applica…

Isoparametric hypersurfaces and their application to special geometries

This lecture will introduce the Support Vector algorithms for classification and regression. They are an application of the so called kernel trick, which allows the extension of a certain class of linear algorithms to the non linear case.…

Comment: Fisher Lecture: Dimension Reduction in Regression [arXiv:0708.3774]

Comment: Fisher Lecture: Dimension Reduction in Regression [arXiv:0708.3774]

Comment: Fisher Lecture: Dimension Reduction in Regression [arXiv:0708.3774]

We show how, using linear-algebraic tools developed to prove Tverberg's theorem in combinatorial geometry, we can design new models of multi-class support vector machines (SVMs). These supervised learning protocols require fewer conditions…

The training of Support Vector Machines may be a very difficult task when dealing with very large datasets. The memory requirement and the time consumption of the SVMs algorithms grow rapidly with the increase of the data. To overcome these…

We apply information-based complexity analysis to support vector machine (SVM) algorithms, with the goal of a comprehensive continuous algorithmic analysis of such algorithms. This involves complexity measures in which some higher order…

Support vector machine (SVM), is a popular kernel method for data classification that demonstrated its efficiency for a large range of practical applications. The method suffers, however, from some weaknesses including; time processing,…

The support vector machines (SVM) algorithm is a popular classification technique in data mining and machine learning. In this paper, we propose a distributed SVM algorithm and demonstrate its use in a number of applications. The algorithm…

Subgradient algorithms for training support vector machines have been quite successful for solving large-scale and online learning problems. However, they have been restricted to linear kernels and strongly convex formulations. This paper…

This is article is taken out.

The paper has been merged into math/0503283

This is a response to the commentaries on "CoRR: A Computing Research Repository".

Some early chapters of the upcoming book "Polyfold Constructions: Tools, Techniques, and Functors"

This is the second half of a two-part series studying tensor categories of unitary vertex operator algebras from a unitary point of view.

Comment: Bayesian Checking of the Second Levels of Hierarchical Models [arXiv:0802.0743]

Comment: Bayesian Checking of the Second Levels of Hierarchical Models [arXiv:0802.0743]

The article presents a new method of integration of functions with values in Banach spaces. This integral and related notions prove to be a useful tool in the study of Banach space geomtry.

This is an overview of various aspects of the 6-vertex model in statistical mechanics and related models.