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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.
Because SVM came from the generalized concept of optimal hyper plane with maximum margin between the two classes.
Its working is based on the principle of finding a hyper plane that gives the maximum margin between the classes of the given data sets.
SVM generates a hyper-plane in the space and uses the maximum margin between these two sets of vectors.
During training, it aims at creating a maximum margin between the classification boundary and the samples closest to this boundary [ 13, 14].
Given a training set belonging to two classes, a linear SVM finds an optimal separating hyper plane with the maximum margin between samples of two classes, by solving the following optimization problem: (2) M i n 1 2 W T W s u b j e c t t o : y i (W. X i + b ) − 1 ≥ 0 where X i and y i are ith sample and its label.
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A support vector machine (SVM) classifier does not estimate class conditional distributions, but rather a maximum margin hyper-plane between the positive and negative examples of the class.
The hyper plane was the best option to separate the data as it yields the maximum margin of separation between the classes.
To avoid a high value of ξ i, some kind of penalty term C was introduced into the original optimization Equation (3), which can be modified as: Minimizefrac{1}{2}{leftVert wrightVert}^2+C{displaystyle sum_{i=1}^n{xi}_i} (8 where C > 0 is the penalty factor to control the trade-off between the maximum margin and the minimum error.
C denotes the misclassification penalty parameter which controls the trade-off between the maximum margin and the minimum error and must be set to a given value [ 11].
Second, ASFDA seeks projections to minimize within-class temporal variation and maximize between-class temporal variation simultaneously based on maximum margin criterion.
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