Sentence examples for maximal margin of from inspiring English sources

Additional advantages of this algorithm include ease of implementation and fast computation; given its incremental character, the number of updates grows as O(n) where n is the number of examples in D. The SVM [27], [33], [38] is a kernel machine that learns a hyperplane with the maximal margin of separation between vectors of two distinctive classes in a RKHS.

All proportions presented have a maximal margin of error of ±4% due to the sample size of 602.

In this dataset, a maximal margin of five experiments relative to random was obtained when 200 models were available.

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).

Sample size was calculated with regard to confidence intervals for estimated population frequencies (95% CI): a maximal margin of error for proportions of ± 6.25% for questions answerable dichotomously was considered narrow enough (i.e. the maximum width of the 95% CI for proportions should be 12.5% for questions with only two response categories e.g. yes/no).

Classification is performed by determining the Euclidian distance of the data set to the n-1 dimensional maximal margin hyperplane (absolute value of the normal vector) and the direction of the vector (class 1 or class 2).

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.

SVM [ 13] separates a set of binary-labeled training data by means of a maximal margin hyperplane, building a decision function R N → ± 1.

So the problem of looking for OSH under maximal margin condition can be converted to searching for the minimum of, constrained by (16).

The underlying idea behind the SVM is to calculate a maximal margin hyperplane that performs a binary classification of the data [22].

SVM was introduced by [ 51] aiming to find the Maximal Margin Hyperplane (MMH) based on the concept of the support vector theory to minimize the error.