上世纪七八十年代,美国科学家提出了一个称为'多传感信息融合'(Multisensor information fusion)或'多源信息融合'(Multi-source information fusion)的全新概念,并迅速扩大到世界范围内,成为当今信息技术领域的一个研...上世纪七八十年代,美国科学家提出了一个称为'多传感信息融合'(Multisensor information fusion)或'多源信息融合'(Multi-source information fusion)的全新概念,并迅速扩大到世界范围内,成为当今信息技术领域的一个研究热点.所谓多源信息融合,就是提出一些理论和方法,对具有相似或不同特征模式的多源信息进行处理,以获得融合信息.简单地说,将多种传感器获得的数据进行所谓的'融合处理'。展开更多
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
文摘上世纪七八十年代,美国科学家提出了一个称为'多传感信息融合'(Multisensor information fusion)或'多源信息融合'(Multi-source information fusion)的全新概念,并迅速扩大到世界范围内,成为当今信息技术领域的一个研究热点.所谓多源信息融合,就是提出一些理论和方法,对具有相似或不同特征模式的多源信息进行处理,以获得融合信息.简单地说,将多种传感器获得的数据进行所谓的'融合处理'。
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.
