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The poll has a maximum margin of sampling error of plus or minus three points.
The maximum margin classifier, aka hard-margin support vector machine (SVM).
Based on the objective function and the maximum margin criterion, a dimensionality reduction algorithm, the non-negative sparse semi-supervised maximum margin algorithm, is proposed.
Since the 1960's the maximum margin loan has been 50percentt of a stock's purchase price.
To address this issue, a novel semi-supervised learning method for dimensionality reduction, namely kernel maximum margin projection (KMMP) is proposed in this paper based on our previous work of maximum margin projection (MMP).
The central bank cut the maximum margin between the base lending rate for commercial banks and the rate charged customers to 2.5 percentage points from 4 points.
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We tested both discriminative Maximum Likelihood and Maximum-Margin methods for parameter estimation.
Algorithm 1: Pseudocode for our 1-slack maximum-margin learning algorithm.
In contrast, maximum-margin training does not learn a calibrated and does not require.
SVM has high generalization capability because it applies a maximum-margin classification function.
A more detailed description of maximum-margin based classifiers can be found in Schölkopf and Smola [14] for further reading.
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