Sentence examples for maximum margin classifier from inspiring English sources

The constraints in this formulation ensure that the maximum margin classifier classifies each example correctly, which is possible since we have assumed that the data are linearly separable.

Therefore, this paper proposes a novel Tensor-based Locally Maximum Margin Classifier (TLMMC), which avoids the kernel projection with unclear physical meaning.

Integrating the attractive characters of tensor representation, local-based method, and maximum margin classifier, TLMMC shows impressive results in both theoretical analysis and empirical validation.

In our case, there are two parameters: the C from the maximum margin classifier and the γ from the radial basis function kernel.

In this paper, the use of a maximum margin classifier using input features extracted from time and frequency domain analysis of the AE data was investigated for the detection of the P-waves.

Finally, the maximum margin classifier is calculated by solving the following constrained optimization problem which is expressed in terms of variables α i : maximize α ∑ i = 1 n α i − 1 2 ∑ i = 1 n ∑ j = 1 n y i y j α i α j x i T x j subject to : ∑ i = 1 n y j α i = 0, 0 ≤ α i ≤ C. The constant C > 0 defines the trade-off between the training error and the margin.

SVMs are known as maximum margin classifiers since they classify their objects by minimizing the empirical generalization error and maximizing the geometric margin simultaneously.

In the past decades, maximum margin classifiers have been recognized as a powerful tool for pattern classification problem.

The performance uncertainty of maximum margin classifiers in real world applications is mainly caused by the lack of theoretical ground for kernel projection, which unfortunately, is just the key of the performance improvement.

The maximum-margin classifier is the discriminate function that maximizes the geometric margin 1/||w||, which is equivalent to minimizing ||w||2.

These features were used in conjunction with a maximum margin support vector machine (SVM) classifier coupled with probabilistic output [6] to recognize the P-waves in the presence of noise for accurate time of arrival (TOA) calculation.