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

Comment on The Place of Death in the Quality of Life [math.ST/0612783]

While there has been some discussion on how Symbolic Computation could be used for AI there is little literature on applications in the other direction. However, recent results for quantifier elimination suggest that, given enough example…

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]

In this paper we promote the use of Support Vector Machines (SVM) as a machine learning tool for searches in high-energy physics. As an example for a new- physics search we discuss the popular case of Supersymmetry at the Large Hadron…

Response to Comment by A. Bussmann-Holder (arXiv:0909.3603)

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…

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…

We respond to comments on our paper, titled "Instrumental variable estimation of the causal hazard ratio."

Reply to Comment on 'Length Scale Dependence of DNA Mechanical Properties'

An importance sampling and bagging approach to solving the support vector machine (SVM) problem in the context of large databases is presented and evaluated. Our algorithm builds on the nearest neighbors ideas presented in Camelo at al.…

Comment in response to Phys. Rev. Lett. 112, 046401 (2014)

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,…

Rejoinder to ``Boosting Algorithms: Regularization, Prediction and Model Fitting'' [arXiv:0804.2752]

A comment on cond-mat/0210707, cond-mat/0208230, cond-mat/0207153, and cond-mat/0202140.

Remarks on mathematical proof and the practice of mathematics.

Some formulas and speculations are presented relative to integrable systems and quantum mechanics.

Comment on ``Microarrays, Empirical Bayes and the Two-Groups Model'' [arXiv:0808.0572]

Comment on ``Microarrays, Empirical Bayes and the Two-Groups Model'' [arXiv:0808.0572]

A relevant reference ([14]) has been added.