相关论文: Comment on "Support Vector Machines with Applicati…

Discussion of ``Least angle regression'' by Efron et al. [math.ST/0406456]

Discussion of ``Least angle regression'' by Efron et al. [math.ST/0406456]

Discussion of ``Least angle regression'' by Efron et al. [math.ST/0406456]

Comment on "Indispensable Finite Time Correlations for Fokker-Planck Equations from Time Series Data"

Lecture notes on quantum machine learning for computer scientists.

We make several comments on "Note on the Analytical Solution of the Rabi Model" (arXiv:1210.4946).

Implied posterior probability of a given model (say, Support Vector Machines (SVM)) at a point $\bf{x}$ is an estimate of the class posterior probability pertaining to the class of functions of the model applied to a given dataset. It can…

A mean field variational Bayes approach to support vector machines (SVMs) using the latent variable representation on Polson & Scott (2012) is presented. This representation allows circumvention of many of the shortcomings associated with…

Support vector machine (SVM) is a well-known statistical technique for classification problems in machine learning and other fields. An important question for SVM is the selection of covariates (or features) for the model. Many studies have…

We give a survey on eta invariants including methods of computation and applications in differential topology.

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

This paper describes an innovative way to optimize a multivariate classifier, in particular a Support Vector Machine algorithm, on a problem characterized by a biased training sample. This is possible thanks to the feedback of a…

The goal of the article is to get a satisfactory theory of cosupport in the derived category $\mathrm{D}(R)$, this is done by introducing another versions of the "big" and "small" cosupport for complexes. We provide some properties for…

This is a reply to the Comment on 'Spin Decoherence in Superconducting Atom Chips' [arXiv:quant-ph/0610095 (2006)].

Inspired by computer assisted proofs in analysis, we present an interval approach to real-number computations.

Support Vector Machines (SVM), a popular machine learning technique, has been applied to a wide range of domains such as science, finance, and social networks for supervised learning. Whether it is identifying high-risk patients by…

Support Vector Machines (SVMs) are popular tools for data mining tasks such as classification, regression, and density estimation. However, original SVM (C-SVM) only considers local information of data points on or over the margin.…

The support vector machine is a flexible optimization-based technique widely used for classification problems. In practice, its training part becomes computationally expensive on large-scale data sets because of such reasons as the…

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.

Estimates of some integrals related to variations of smooth functions are presented.