相关论文: Comment on "Support Vector Machines with Applicati…
Comment: Struggles with Survey Weighting and Regression Modeling [arXiv:0710.5005]
Comment: Struggles with Survey Weighting and Regression Modeling [arXiv:0710.5005]
Comment: Struggles with Survey Weighting and Regression Modeling [arXiv:0710.5005]
We explore the merits of training of support vector machines for binary classification by means of solving systems of ordinary differential equations. We thus assume a continuous time perspective on a machine learning problem which may be…
There has been growing interest in extending traditional vector-based machine learning techniques to their tensor forms. An example is the support tensor machine (STM) that utilizes a rank-one tensor to capture the data structure, thereby…
It is shown that the numerical data in cond-mat/0608362 are in very good agreement with the predictions of cond-mat/0601573.
Comment on "Biases in the Quasar Mass-Luminosity Plane"
Comment on 'Path Summation Formulation of the Master Equation'
Support vector machines (SVMs) appeared in the early nineties as optimal margin classifiers in the context of Vapnik's statistical learning theory. Since then SVMs have been successfully applied to real-world data analysis problems, often…
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)
In this paper there is proposed a generalized version of the SVM for binary classification problems in the case of using an arbitrary transformation x -> y. An approach similar to the classic SVM method is used. The problem is widely…
Comment on Classifier Technology and the Illusion of Progress--Credit Scoring [math.ST/0606441]
Contributed discussion and rejoinder to "Geodesic Monte Carlo on Embedded Manifolds" (arXiv:1301.6064)
Comment on ``Understanding OR, PS and DR'' [arXiv:0804.2958]
We review the concept of support vector machines (SVMs) and discuss examples of their use. One of the benefits of SVM algorithms, compared with neural networks and decision trees is that they can be less susceptible to over fitting than…
Comment on "The Need for Syncretism in Applied Statistics" [arXiv:1012.1161]
Please goto the "Note Added" part of v6, quant-ph/0501143
In \cite{simon2023algorithms} we introduced four algorithms for the training of neural support vector machines (NSVMs) and demonstrated their feasibility. In this note we introduce neural quantum support vector machines, that is, NSVMs with…
This is a comment on Phys. Rev. A 67, 022104(2003).