Related papers: Comment on "Support Vector Machines with Applicati…

This is a companion piece to my paper on "Example-Based Procedural Modeling Using Graph Grammars." This paper examines some of the theoretical issues in more detail. This paper discusses some more complex parts of the implementation, why…

Presentation of set matrices and demonstration of their efficiency as a tool using the path/cycle problem.

We comment on the paper "Teleportation with a uniformly accelerated partner" (quant-ph/0302179).

Remarks on reply (cond-mat/0206368) to Johansen's comment (cond-mat/0205249)

Rejoinder: Monitoring Networked Applications With Incremental Quantile Estimation [arXiv:0708.0302]

Isoparametric hypersurfaces and their application to special geometries

It is shown that bootstrap approximations of support vector machines (SVMs) based on a general convex and smooth loss function and on a general kernel are consistent. This result is useful to approximate the unknown finite sample…

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

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]

Accurate transient stability assessment is a crucial prerequisite for proper power system operation and planning with various operational constraints. Transient stability assessment of modern power systems is becoming very challenging due…

We tackle the problem of computing counterfactual explanations -- minimal changes to the features that flip an undesirable model prediction. We propose a solution to this question for linear Support Vector Machine (SVMs) models. Moreover,…

We give a survey of the foundations of statistical queries and their many applications to other areas. We introduce the model, give the main definitions, and we explore the fundamental theory statistical queries and how how it connects to…

Support Vector Machines (SVMs) with various kernels have played dominant role in machine learning for many years, finding numerous applications. Although they have many attractive features interpretation of their solutions is quite…

We propose a method for support vector machine classification using indefinite kernels. Instead of directly minimizing or stabilizing a nonconvex loss function, our algorithm simultaneously computes support vectors and a proxy kernel matrix…

We present an efficient coreset construction algorithm for large-scale Support Vector Machine (SVM) training in Big Data and streaming applications. A coreset is a small, representative subset of the original data points such that a models…

Support vector machines (SVMs) are special kernel based methods and belong to the most successful learning methods since more than a decade. SVMs can informally be described as a kind of regularized M-estimators for functions and have…

Comment on ``Gibbs Sampling, Exponential Families and Orthogonal Polynomials'' [arXiv:0808.3852]