Sentence examples for maximal margin and from inspiring English sources

With regard to binary classification, the SVM trains a classifier by mapping the input samples onto a high-dimensional space through kernel functions, and then seeking a separating hyperplane that differentiates the two classes with maximal margin and minimal error.

The SVM algorithm trains a classifier by representing the input feature vectors, using a kernel function in the majority of cases, onto a high-dimensional space, and then selects a discriminating hyperplane that separates the two classes with maximal margin and the least error.

To classify the data, the SVM trains a classifier by mapping the input samples, using a kernel function in most cases, onto a high-dimensional space, and then seeking a separating hyperplane that differentiates the two classes with maximal margin and minimal error.

Based on binary classification, the concept of SVM is to map the input samples into a higher dimensional space using a kernel function and then to find a hyperplane that discriminates between the two classes with maximal margin and minimal error.

Classification is performed by determining the Euclidian distance (defined as the SVM score) of the polypeptides to the (n-1) dimensional maximal margin hyperplane and the direction of the vector.

Different machine learning approaches have been employed to analyze microarray data including k-nearest-neighbors [6], [10], artificial neural networks [8], support vector machines [11] [13], maximal margin linear programming [14], and random forest [15].

Then, within the feature space, seek an optimized linear division; i.e. construct a hyper-plane that can separate the entire samples into two classes with the least errors and maximal margin.

The underlying idea of SVM classifiers is to calculate a maximal margin hyperplane separating the cases and controls.

We show by means of both synthetic and real world data sets that the proposed SVP is able to provide very accurate results in many classification problems, providing maximal margin solutions when classes are separable, and also producing very compact architectures comparable to classical multilayer perceptrons.

Classification of patient samples with the rejection pattern is expressed as membership probability and quantified by the Euclidean distance of the data point to the maximal margin of a separation hyperplane between cases and controls in a multidimensional space constructed by the peptide classifiers (by the use of Support Vector Machines, SVM).

If a fire occurs, the objective is to detect the associated fire signatures at the earliest possible time from inception, thus minimizing propagation and collateral damage while providing maximal margin for suppression.