医学训练式治疗(medical training therapy,MTT)是在欧洲独立发展起来的一个康复体系,致力于功能锻炼和形态适应,而作为发源于本土,传统康复疗法中重要的组成部分之一的五禽戏功法,长于强身健体,养身康复。MTT和五禽戏功法二者"异...医学训练式治疗(medical training therapy,MTT)是在欧洲独立发展起来的一个康复体系,致力于功能锻炼和形态适应,而作为发源于本土,传统康复疗法中重要的组成部分之一的五禽戏功法,长于强身健体,养身康复。MTT和五禽戏功法二者"异源同功",本文初步探讨了MTT和五禽戏功法共同的康复理念和以主动为主的运动方式以及趋于个体化的训练方式,而寻求二者在技术上和观念上的结合能更好地拓展中医传统康复治疗在康复临床上的运用。展开更多
Many data mining applications have a large amount of data but labeling data is usually difficult, expensive, or time consuming, as it requires human experts for annotation. Semi-supervised learning addresses this prob...Many data mining applications have a large amount of data but labeling data is usually difficult, expensive, or time consuming, as it requires human experts for annotation. Semi-supervised learning addresses this problem by using unlabeled data together with labeled data in the training process. Co-Training is a popular semi-supervised learning algorithm that has the assumptions that each example is represented by multiple sets of features （views） and these views are sufficient for learning and independent given the class. However, these assumptions axe strong and are not satisfied in many real-world domains. In this paper, a single-view variant of Co-Training, called Co-Training by Committee （CoBC） is proposed, in which an ensemble of diverse classifiers is used instead of redundant and independent views. We introduce a new labeling confidence measure for unlabeled examples based on estimating the local accuracy of the committee members on its neighborhood. Then we introduce two new learning algorithms, QBC-then-CoBC and QBC-with-CoBC, which combine the merits of committee-based semi-supervised learning and active learning. The random subspace method is applied on both C4.5 decision trees and 1-nearest neighbor classifiers to construct the diverse ensembles used for semi-supervised learning and active learning. Experiments show that these two combinations can outperform other non committee-based ones.展开更多
文摘医学训练式治疗(medical training therapy,MTT)是在欧洲独立发展起来的一个康复体系,致力于功能锻炼和形态适应,而作为发源于本土,传统康复疗法中重要的组成部分之一的五禽戏功法,长于强身健体,养身康复。MTT和五禽戏功法二者"异源同功",本文初步探讨了MTT和五禽戏功法共同的康复理念和以主动为主的运动方式以及趋于个体化的训练方式,而寻求二者在技术上和观念上的结合能更好地拓展中医传统康复治疗在康复临床上的运用。
基金partially supported by the Transregional Collaborative Research Centre SFB/TRR 62 Companion-Technology for Cognitive Technical Systems funded by the German Research Foundation(DFG)supported by a scholarship of the German Academic Exchange Service(DAAD)
文摘Many data mining applications have a large amount of data but labeling data is usually difficult, expensive, or time consuming, as it requires human experts for annotation. Semi-supervised learning addresses this problem by using unlabeled data together with labeled data in the training process. Co-Training is a popular semi-supervised learning algorithm that has the assumptions that each example is represented by multiple sets of features （views） and these views are sufficient for learning and independent given the class. However, these assumptions axe strong and are not satisfied in many real-world domains. In this paper, a single-view variant of Co-Training, called Co-Training by Committee （CoBC） is proposed, in which an ensemble of diverse classifiers is used instead of redundant and independent views. We introduce a new labeling confidence measure for unlabeled examples based on estimating the local accuracy of the committee members on its neighborhood. Then we introduce two new learning algorithms, QBC-then-CoBC and QBC-with-CoBC, which combine the merits of committee-based semi-supervised learning and active learning. The random subspace method is applied on both C4.5 decision trees and 1-nearest neighbor classifiers to construct the diverse ensembles used for semi-supervised learning and active learning. Experiments show that these two combinations can outperform other non committee-based ones.
