Subjects: Mathematics >> Theoretical Computer Science submitted time 2017-11-17
Abstract: This paper proposes a new linear classification method named Focusing Classification, with the goal of taking the place of Logistic Regression. Focusing Classification has some advantages: length of its normal vector is limited, intuitional geometrical explanation, parameters' initial values are close to the best values. numerical experiments on the MNIST dataset demonstrate that Focusing Classification has better performance than Logistic Regression on length of its normal vector, accuracy and rate of convergence. With initial parameter values, Focusing Classification gains an accuracy of 97.31%.
Peer Review Status:Awaiting Review