Reducing the Solution of Support Vector Machines Using Simulated Annealing Algorithm
Jih Pin Yeh, Chiang Ming Chiang · 2017
Support vector Machines are a relatively recent machine learning technique. One of the SVM problems is that SVM is currently considerably slower in test phase caused by the large number of the support vectors, which greatly influences it into the practical use. To address this problem, we proposed a simulated annealing algorithm to reduce the solutions for an SVM by selecting vectors from the trained support vector solutions, such that the selected vectors best approximate the original discriminant function. Experimental results show that the proposed method can reduce the solutions for an SVM by selecting vectors from the trained support vector solutions, confirm the theoretical results and improve classification accuracy.