Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: Twin support vector machine with two nonparallel classifying hyperplanes and its extensions have attracted much attention in machine learning and data mining. Hover, the prediction accuracy may be highly influenced when noise is involved. In particular, for the least squares case, the intractable computational burden may be incurred for large scale data. To address the above problems,  propose the double-ighted least squares twin bounded support vector machines and develop the online learning algorithms. By introducing the double-ighted mechanism, the linear and nonlinear double-ighted learning models are proposed to reduce the influence of noise. The online learning algorithms for solving the two models are developed, which can avoid computing the inverse of the large scale matrices. Furthermore, a new pruning mechanism which can avoid updating the kernel matrices in every iteration step for solving nonlinear model is also developed. Simulation results on three UCI data with noise demonstrate that the online learning algorithm for the linear double-ighted learning model can get least computation time as ll considerable classification accuracy. Simulation results on UCI data and two-moons data with noise demonstrate that the nonlinear double-ighted learning model can be effectively solved by the online learning algorithm with the pruning mechanism.
['Juntao Li', 'Yimin Cao', 'Y. Q. Wang', 'Huimin Xiao']
I am writing a research paper on a idea Online Learning Algorithms for Double-Weighted Least Squares Twin Bounded Support Vector Machines
[{"from": "human", "value": "I am writing a research paper on a idea Online Learning Algorithms for Double-Weighted Least Squares Twin Bounded Support Vector Machines"}, {"from": "gpt", "value": "Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: Twin support vector machine with two nonparallel classifying hyperplanes and its extensions have attracted much attention in machine learning and data mining. Hover, the prediction accuracy may be highly influenced when noise is involved. In particular, for the least squares case, the intractable computational burden may be incurred for large scale data. To address the above problems,  propose the double-ighted least squares twin bounded support vector machines and develop the online learning algorithms. By introducing the double-ighted mechanism, the linear and nonlinear double-ighted learning models are proposed to reduce the influence of noise. The online learning algorithms for solving the two models are developed, which can avoid computing the inverse of the large scale matrices. Furthermore, a new pruning mechanism which can avoid updating the kernel matrices in every iteration step for solving nonlinear model is also developed. Simulation results on three UCI data with noise demonstrate that the online learning algorithm for the linear double-ighted learning model can get least computation time as ll considerable classification accuracy. Simulation results on UCI data and two-moons data with noise demonstrate that the nonlinear double-ighted learning model can be effectively solved by the online learning algorithm with the pruning mechanism."}]