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
Globalization had changed the competitive landscape in which entrepreneurs used to compete. There are advantages and disadvantages of doing business globally. Globalization had also brought about many challenges to en...Globalization had changed the competitive landscape in which entrepreneurs used to compete. There are advantages and disadvantages of doing business globally. Globalization had also brought about many challenges to entrepreneurs in the management of their organization. The values and beliefs of managers and staff across the globe influence visions, missions of their organization. Culture, resources and business practices of countries vary widely. Entrepreneurs must deal with global issues side by side with domestic considerations. This paper looks into how the theories and model of strategic management remain useful and relevant to entrepreneurs in a globalized business world especially in a turbulent economy.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Globalization had changed the competitive landscape in which entrepreneurs used to compete. There are advantages and disadvantages of doing business globally. Globalization had also brought about many challenges to entrepreneurs in the management of their organization. The values and beliefs of managers and staff across the globe influence visions, missions of their organization. Culture, resources and business practices of countries vary widely. Entrepreneurs must deal with global issues side by side with domestic considerations. This paper looks into how the theories and model of strategic management remain useful and relevant to entrepreneurs in a globalized business world especially in a turbulent economy.
