Sentence examples for maximum margin classification from inspiring English sources
It works on statistical machine learning principle, deriving maximum margin classification decision boundary while considering multiple variables, which in the present work has been plant specific dinucleotide density profiles variations with respect to the possible target position.
SVM has high generalization capability because it applies a maximum-margin classification function.
During training, it aims at creating a maximum margin between the classification boundary and the samples closest to this boundary [ 13, 14].
However, for image and video data, it is still hard to map the visual features to the semantic labels, although many maximum margin techniques have announced impressive classification results in their own datasets.
The SVM is a maximum margin classifier that can solve non-linear classification problems by learning an optimal separating hyperplane in a higher-dimensional feature space.
It transfers the ranking problem into a partial order pair classification problem, and utilizes the maximum margin optimization in SVM to derive the optimal ranking order.
SVM is a classification method that uses a maximum margin hyperplane from a set of hyperplanes in a high-dimensional space to separate two or more classes by distancing the plane from the closest data point of all classes.
In this paper, we focus on the feature weighting problem in a specific machine learning area: multiple-instance learning, and propose maximum margin multiple-instance feature weighting (M3IFW) to seek large classification margins in the weighted feature space.
The Eq. (3) below represents Lagrange multiplier for estimating maximum margin and it is used in a general SVM-based classification model.
In the past decades, maximum margin classifiers have been recognized as a powerful tool for pattern classification problem.
An SVM solves this difficulty by mapping and converting the input space into a high-dimensional space; after that it finds a linear classification model to classify the input data with a maximum margin hyperplane.
