相关论文: Comment on "Support Vector Machines with Applicati…
Comment on "Citation Statistics" [arXiv:0910.3529]
In this paper, we study the support vector machine and introduced the notion of generalized support vector machine for classification of data. We show that the problem of generalized support vector machine is equivalent to the problem of…
Comment on ``Lancaster Probabilities and Gibbs Sampling'' [arXiv:0808.3852]
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…
Comment on "Biases in the Quasar Mass-Luminosity Plane"
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 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…
Rejoinder: Classifier Technology and the Illusion of Progress [math.ST/0606441]
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…
Multivariate data analysis techniques have the potential to improve physics analyses in many ways. The common classification problem of signal/background discrimination is one example. The Support Vector Machine learning algorithm is a…
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…
Comment on Classifier Technology and the Illusion of Progress [math.ST/0606441]
Comment on Classifier Technology and the Illusion of Progress [math.ST/0606441]
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…
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…
We respond to comments on our paper, titled "Instrumental variable estimation of the causal hazard ratio."
Comment on ``Understanding OR, PS and DR'' [arXiv:0804.2958]
This is a Comment on "Universal Fluctuations in Correlated Systems".
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…