摘要
动作识别是康复中心研究领域的一个热门话题。机器学习是动作识别的一个重要方面。由于样本标注需要付出诸多人工努力,所以被标注的样本数量是有限的。而未被标注样本数量是庞大缘于它容易获取,无需人为注解。训练数据是基于半监督学习动作识别的核心。文章将着重强调数据选择策略和扩展度,这也是训练数据选择的基础。文章结合已标注的有限样本,利用未被标注样本来提高动作识别的精度。
Activity recognition is a hot topic in Healthcare.Machine learning is a key aspect in activity recognition.Since the number of labeled samples is limited because they require the efforts of human annotators,while the number of unlabelled data is huge because they are easy to get without human＇s labeling effort.The training data is the centre of the semi-supervised based activity recognition.This paper emphasize the selection strategy and enlarge degree,which are the basic of the training data selection.This paper provide a method to utilize the available unlabeled samples to enhance the performance of activity learning with a limited number of labeled samples.
出处
《计算机与数字工程》
2010年第11期22-25,37,共5页
Computer & Digital Engineering
基金
江苏省高校自然科学基金项目(编号:08KJD520018)
南京信息工程大学自然科学基金项目(编号:20080302)资助
关键词
动作识别
半监督学习
数据选择策略
扩展度
activity recognition
semi-supervised learning
data selection strategy
enlarge degree
