The main aim for a 2D spiral recognition algorithm is to learn to discriminate between data distributed on two distinct strands in the x-y plane.This problem is of critical importance since it incorporates temporal ch...The main aim for a 2D spiral recognition algorithm is to learn to discriminate between data distributed on two distinct strands in the x-y plane.This problem is of critical importance since it incorporates temporal characteristics often found in real-time applications.Previous work with this benchmark has witnessed poor results with statistical methods such as discriminant analysis and tedious procedures for better results with neural networks.This paper presents a max-density covering learning algorithm based on constructive neural networks which is efficient in terms of the recognition rate and the speed of recognition.The results show that it is possible to solve the spiral problem instantaneously(up to 100% correct classification on the test set).展开更多
The curse of high-dimensionality has emerged in the statistical fields more and more frequently.Many techniques have been developed to address this challenge for classification problems. We propose a novel feature scr...The curse of high-dimensionality has emerged in the statistical fields more and more frequently.Many techniques have been developed to address this challenge for classification problems. We propose a novel feature screening procedure for dichotomous response data. This new method can be implemented as easily as t-test marginal screening approach, and the proposed procedure is free of any subexponential tail probability conditions and moment requirement and not restricted in a specific model structure. We prove that our method possesses the sure screening property and also illustrate the effect of screening by Monte Carlo simulation and apply it to a real data example.展开更多
基金Sponsored by the National High Technology Research Development Program of China(Grant No.2001AA413130).
文摘The main aim for a 2D spiral recognition algorithm is to learn to discriminate between data distributed on two distinct strands in the x-y plane.This problem is of critical importance since it incorporates temporal characteristics often found in real-time applications.Previous work with this benchmark has witnessed poor results with statistical methods such as discriminant analysis and tedious procedures for better results with neural networks.This paper presents a max-density covering learning algorithm based on constructive neural networks which is efficient in terms of the recognition rate and the speed of recognition.The results show that it is possible to solve the spiral problem instantaneously(up to 100% correct classification on the test set).
基金supported by Graduate Innovation Foundation of Shanghai University of Finance and Economics of China (Grant Nos. CXJJ-2014-459 and CXJJ-2015-430)National Natural Science Foundation of China (Grant No. 71271128), the State Key Program of National Natural Science Foundation of China (Grant No. 71331006), the State Key Program in the Major Research Plan of National Natural Science Foundation of China (Grant No. 91546202)+1 种基金National Center for Mathematics and Interdisciplinary Sciences, Key Laboratory of Random Complex Structures and Data Science, Academy of Mathematics and Systems Science, Chinese Academy of Sciences (Grant No. 2008DP173182)Innovative Research Team in Shanghai University of Finance and Economics (Grant No. IRT13077)
文摘The curse of high-dimensionality has emerged in the statistical fields more and more frequently.Many techniques have been developed to address this challenge for classification problems. We propose a novel feature screening procedure for dichotomous response data. This new method can be implemented as easily as t-test marginal screening approach, and the proposed procedure is free of any subexponential tail probability conditions and moment requirement and not restricted in a specific model structure. We prove that our method possesses the sure screening property and also illustrate the effect of screening by Monte Carlo simulation and apply it to a real data example.
