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

This paper presents a short evaluation about the integration of information derived from wavelet non-linear-time-invariant (non-LTI) projection properties using Support Vector Machines (SVM). These properties may give additional information…

Support vector machine (SVM) is one of the most studied paradigms in the realm of machine learning for classification and regression problems. It relies on vectorized input data. However, a significant portion of the real-world data exists…

Comment: Expert Elicitation for Reliable System Design [arXiv:0708.0279]

Comment: Expert Elicitation for Reliable System Design [arXiv:0708.0279]

Comment: Expert Elicitation for Reliable System Design [arXiv:0708.0279]

This is a comment on Phys. Rev. A 67, 022104(2003).

Supplementary Material for "Estimation of a Multiplicative Correlation Structure in the Large Dimensional Case"

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

We present a new approach to obtaining photometric redshifts using a kernel learning technique called Support Vector Machines (SVMs). Unlike traditional spectral energy distribution fitting, this technique requires a large and…

In this note, we have shown special case on Routh stability criterion, which is not discussed, in previous literature. This idea can be useful in computer science applications.

Support vector machines represent a promising development in machine learning research that is not widely used within the remote sensing community. This paper reports the results of Multispectral(Landsat-7 ETM+) and Hyperspectral DAIS)data…

Rejoinder: Conditional Growth Charts [math.ST/0702634]

Comment on "Efficiency of Isothermal Molecular Machines at Maximum Power" (PRL 108, 210602 (2012), arXiv:1201.6396)

In many applications, input data are sampled functions taking their values in infinite dimensional spaces rather than standard vectors. This fact has complex consequences on data analysis algorithms that motivate modifications of them. In…

This paper comments on the published work dealing with robustness and regularization of support vector machines (Journal of Machine Learning Research, vol. 10, pp. 1485-1510, 2009) [arXiv:0803.3490] by H. Xu, etc. They proposed a theorem to…

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]

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

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