Data Classification Using the Robbins-Monro Stochastic Approximation Algorithm
이재국, 고춘택, 최원호 · 전력전자학술대회논문집 · 2005
This paper presents a new data classification method using the Robbins Monro stochastic approximation algorithm, k-nearest neighbor and distribution analysis. To cluster the data set, we decide the centroid of the test data set using k-nearest neighbor algorithm and the local area of data set. To decide each class of the data, the Robbins Monro stochastic approximation algorithm is applied to the decided local area of the data set. To evaluate the performance, the proposed classification method is compared to the conventional fuzzy c-mean method and k-nn algorithm. The simulation results show that the proposed method is more accurate than fuzzy c-mean method, k-nn algorithm and discriminant analysis algorithm.