PMC:4331676 / 16032-16877 JSONTXT

{"project":"2_test","denotations":[{"id":"25708928-16284202-14842651","span":{"begin":659,"end":661},"obj":"16284202"},{"id":"25708928-24931825-14842652","span":{"begin":662,"end":664},"obj":"24931825"},{"id":"25708928-24504871-14842652","span":{"begin":662,"end":664},"obj":"24504871"},{"id":"25708928-19046430-14842652","span":{"begin":662,"end":664},"obj":"19046430"}],"text":"Where xi is a feature vector labeled by yi ∈ {-1, +1} and C, called the cost, is the penalty parameter of the error term. The above model called soft-margin SVM can be able to tolerate noise within the data, which analyze an example by generating a separating hyper-plane with f(x) = ϕ(x)·w + b = 0. Through resolving the above model with lagrangian multiplier method, we obtain w=∑jαh*yj*ϕ(xj) and w⋅ϕ(xi)=∑jαh*yj*ϕ(xj)⋅ϕ(xi), which provides an efficient approach to solve SVM without the explicit use of the non-linear transformation, where K(xi,xj)=ϕ(xi)⋅ϕ(xj)is the kernel function. Application of SVM in bioinformatics problems has been widely explored [15,67-69]. At present, the publicly available LIBSVM, which take the radial basis function (RBF) as the kernel function, is employed as the implementation of SVM. RBF is defined as below"}