Kernel-based methods work by embedding the data into a feature space and then searching linear hypothesis among the embedding data points. The performance is mostly affected by which kernel is used. A promising way is...Kernel-based methods work by embedding the data into a feature space and then searching linear hypothesis among the embedding data points. The performance is mostly affected by which kernel is used. A promising way is to learn the kernel from the data automatically. A general regularized risk functional （RRF） criterion for kernel matrix learning is proposed. Compared with the RRF criterion, general RRF criterion takes into account the geometric distributions of the embedding data points. It is proven that the distance between different geometric distdbutions can be estimated by their centroid distance in the reproducing kernel Hilbert space. Using this criterion for kernel matrix learning leads to a convex quadratically constrained quadratic programming （QCQP） problem. For several commonly used loss functions, their mathematical formulations are given. Experiment results on a collection of benchmark data sets demonstrate the effectiveness of the proposed method.展开更多
Accidents and injuries related to work are major occupational health problems in most of the industrialized countries.Traditional approaches to manage workplace safety in mines have mainly focused on job redesign and ...Accidents and injuries related to work are major occupational health problems in most of the industrialized countries.Traditional approaches to manage workplace safety in mines have mainly focused on job redesign and technical aspects of engineering systems.It is being realized that compliance to rules and regulations of mines is a prerequisite;however,it is not sufficient to achieve further reduction in accident and injury rates in mines.Proactive approaches are necessary to further improve the safety standards in mines.Unsafe conditions and practices in mines lead to a number of accidents,which in turn may cause loss and injury to human lives,damages to property,and loss of production.Hazard identification and risk assessment is an important task for the mining industry which needs to consider all the risk factors at workplaces.Applications of risk management approaches in mines are necessary to identify and quantify potential hazards and to suggest effective solutions.In this paper,the following risk estimation techniques were discussed:(i)DGMS(Directorate General of Mines Safety,India)risk rating criterion,and(ii)a matrix based approach.The proposed tools were demonstrated through an application in an opencast coal mine in India.It was inferred that the risk assessment approach can be used as an effective tool to indentify and control hazards in mines.展开更多
基金supported by the National Natural Science Fundation of China (60736021)the Joint Funds of NSFC-Guangdong Province(U0735003)
文摘Kernel-based methods work by embedding the data into a feature space and then searching linear hypothesis among the embedding data points. The performance is mostly affected by which kernel is used. A promising way is to learn the kernel from the data automatically. A general regularized risk functional （RRF） criterion for kernel matrix learning is proposed. Compared with the RRF criterion, general RRF criterion takes into account the geometric distributions of the embedding data points. It is proven that the distance between different geometric distdbutions can be estimated by their centroid distance in the reproducing kernel Hilbert space. Using this criterion for kernel matrix learning leads to a convex quadratically constrained quadratic programming （QCQP） problem. For several commonly used loss functions, their mathematical formulations are given. Experiment results on a collection of benchmark data sets demonstrate the effectiveness of the proposed method.
文摘Accidents and injuries related to work are major occupational health problems in most of the industrialized countries.Traditional approaches to manage workplace safety in mines have mainly focused on job redesign and technical aspects of engineering systems.It is being realized that compliance to rules and regulations of mines is a prerequisite;however,it is not sufficient to achieve further reduction in accident and injury rates in mines.Proactive approaches are necessary to further improve the safety standards in mines.Unsafe conditions and practices in mines lead to a number of accidents,which in turn may cause loss and injury to human lives,damages to property,and loss of production.Hazard identification and risk assessment is an important task for the mining industry which needs to consider all the risk factors at workplaces.Applications of risk management approaches in mines are necessary to identify and quantify potential hazards and to suggest effective solutions.In this paper,the following risk estimation techniques were discussed:(i)DGMS(Directorate General of Mines Safety,India)risk rating criterion,and(ii)a matrix based approach.The proposed tools were demonstrated through an application in an opencast coal mine in India.It was inferred that the risk assessment approach can be used as an effective tool to indentify and control hazards in mines.
