ALGORITHM OF Ε-SVR BASED ON a LARGE-SCALE SAMPLE SET: STEP-BY-STEP SEARCH

Abstract

In view of the support vectors of ε-SVR that are not distributed in the ε belt and only located on the outskirts of the ε belt, a novel algorithm to construct ε-SVR of a large-scale training sample set is proposed in this paper. It computes firstly the ε-SVR hyper-plane of a small training sample set and the distances d of all samples to the hyperplane, then deletes the samples not in field ε ≤ d ≤ dmax and searches SVs gradually in the scope ε ≤ d ≤ dmax, and trains step-by-step the final ε-SVR. Finally, it analyzes the time complexity of the algorithm, and verifies its convergence in the theory and tests its efficiency by the simulation.

$$\overline{\text {pin}}$$-TSVM: A Robust Transductive Support Vector Machine and its Application to the Detection of COVID-19 Infected Patients

“…We then use the weight vector and the bias term to find the in case of linear vector. In Algorithm 3, we first train the SVM using svmtrain () function using LIBSVM whose time complexity is , where n represents the number of instances in a data set [ 12 ], and then we use [ 9 ] to implement Step 7 in Algorithm 3, whose time complexity is again [ 48 ]. Therefore, the overall time complexity of the Algorithm 3 is .…”

“…We observe that the dual form of -TSVM perform better than rest of the techniques on most data sets. Note that for SVM and TSVM, we implement the dual forms of these techniques for small data sets only as the computational time depends on the number of examples [ 48 ], so we cannot use it for large-scale data sets. We implement the primal form using SGD (Algorithm 1) for large real-world data sets.…”