Tensor representation is useful to reduce the overfitting problem in vector-based learning algorithm in pattern recognition.This is mainly because the structure information of objects in pattern analysis is a reasonab...Tensor representation is useful to reduce the overfitting problem in vector-based learning algorithm in pattern recognition.This is mainly because the structure information of objects in pattern analysis is a reasonable constraint to reduce the number of unknown parameters used to model a classifier.In this paper, we generalize the vector-based learning algorithm TWin Support Vector Machine(TWSVM) to the tensor-based method TWin Support Tensor Machines(TWSTM), which accepts general tensors as input.To examine the effectiveness of TWSTM, we implement the TWSTM method for Microcalcification Clusters(MCs) detection.In the tensor subspace domain, the MCs detection procedure is formulated as a supervised learning and classification problem, and TWSTM is used as a classifier to make decision for the presence of MCs or not.A large number of experiments were carried out to evaluate and compare the performance of the proposed MCs detection algorithm.By comparison with TWSVM, the tensor version reduces the overfitting problem.展开更多
克朗斯股份公司将在2009年曼谷举行的亚洲包装技术展览会（ProPak Asia 2009）上，展示其闻名全球的克朗斯贴标系统。许多由克朗斯技术检验认可并颇具代表性的高质量容器装备也将在103展厅Q1展位展出。其中包括一台装有两个APS3贴标机组...克朗斯股份公司将在2009年曼谷举行的亚洲包装技术展览会（ProPak Asia 2009）上，展示其闻名全球的克朗斯贴标系统。许多由克朗斯技术检验认可并颇具代表性的高质量容器装备也将在103展厅Q1展位展出。其中包括一台装有两个APS3贴标机组的Autocol贴标机以及检测贴标的Checkmat731检测机。展开更多
基金Supported by the National Natural Science Foundation of China (No. 60771068)the Natural Science Basic Research Plan in Shaanxi Province of China (No. 2007F248)
文摘Tensor representation is useful to reduce the overfitting problem in vector-based learning algorithm in pattern recognition.This is mainly because the structure information of objects in pattern analysis is a reasonable constraint to reduce the number of unknown parameters used to model a classifier.In this paper, we generalize the vector-based learning algorithm TWin Support Vector Machine(TWSVM) to the tensor-based method TWin Support Tensor Machines(TWSTM), which accepts general tensors as input.To examine the effectiveness of TWSTM, we implement the TWSTM method for Microcalcification Clusters(MCs) detection.In the tensor subspace domain, the MCs detection procedure is formulated as a supervised learning and classification problem, and TWSTM is used as a classifier to make decision for the presence of MCs or not.A large number of experiments were carried out to evaluate and compare the performance of the proposed MCs detection algorithm.By comparison with TWSVM, the tensor version reduces the overfitting problem.
文摘克朗斯股份公司将在2009年曼谷举行的亚洲包装技术展览会（ProPak Asia 2009）上，展示其闻名全球的克朗斯贴标系统。许多由克朗斯技术检验认可并颇具代表性的高质量容器装备也将在103展厅Q1展位展出。其中包括一台装有两个APS3贴标机组的Autocol贴标机以及检测贴标的Checkmat731检测机。
