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9篇 您的检索式：作者名="Tian Shengfeng"

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		题名	作者	年代	出处	被引量
	1	Learning General Gaussian Kernels by Optimizing Kernel Polarization显示文摘	WANG Tinghua HUANG Houkuan TIAN Shengfeng DENG Dayong	2009	Chinese Journal of Electronics2009，18，2：	4
	2	XSWRL: an extended semantic Web rule language and prototype implemen- tation显示文摘	Li Wan Tian Shengfeng	2011	Expert Systems with Applications2011，38，3：	1
	3	Network forensics based on fuzzy logic and expert system显示文摘	Liao Niandong Tian Shengfeng Wang Tinghua	2009	Computer Communica- tions2009，32，17：	1
	4	Feature selection for SVM via optimization of kernel polarization with Gaussian ARD kernels显示文摘	WANG Tinghua HUANG Houkuan TIAN Shengfeng	2010	Expert Systems with Applications2010，37，9：	1
	5	Feature selec- tion for text classification with Na'fve Bayes 显示文摘	Jingnian Chen Houkuan Huang Shengfeng Tian	2009	Expert Systems with Applications2009，36，3：	1
	6	A Novel Radial Basis Function Neural Network Classifier with Centers Set By Cooperative Clustering 显示文摘	Mu Shaomin Tian Shengfeng Yin Chuanhuan	2007	International Journal of Fuzzy Systems (S1562-2479)2007，9，4：	1
	7	Feature selection for text classification with Na lve Bayes显示文摘	Jingnian Chen Houkuan Huang Shengfeng Tian Youli Qu	2009	Expert Systems with Applications2009，36，3：	1
	8	Feature selection for SVM via optimization of kernel polarization with Gaussian ARD ker- nels显示文摘	Tinghua Wang Houkuan Huang Shengfeng Tian	2010	Expert System with Applications2010，37，：	1
	9	Exploring Local Regularities for 3D Object Recognition显示文摘In order to find better simplicity measurements for 3D object recognition, a new set of local regularities is developed and tested in a stepwise 3D reconstruction method, including localized minimizing standard deviation of angles(L-MSDA), localized minimizing standard deviation of segment magnitudes(L-MSDSM), localized minimum standard deviation of areas of child faces(L-MSDAF), localized minimum sum of segment magnitudes of common edges(L-MSSM), and localized minimum sum of areas of child face(L-MSAF). Based on their effectiveness measurements in terms of form and size distortions, it is found that when two local regularities: L-MSDA and L-MSDSM are combined together, they can produce better performance. In addition, the best weightings for them to work together are identified as 10% for L-MSDSM and 90% for L-MSDA. The test results show that the combined usage of L-MSDA and L-MSDSM with identified weightings has a potential to be applied in other optimization based 3D recognition methods to improve their efficacy and robustness.	TIAN Huaiwen QIN Shengfeng	2016	Chinese Journal of Mechanical Engineering2016，29，6：	0

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