Tri-training能有效利用无标记样例提高泛化能力.针对Tri-training迭代中无标记样例常被错误标记而形成训练集噪声,导致性能不稳定的缺点,文中提出ADE-Tri-training(Tri-training with Adaptive Data Editing)新算法.它不仅利用Remove O...Tri-training能有效利用无标记样例提高泛化能力.针对Tri-training迭代中无标记样例常被错误标记而形成训练集噪声,导致性能不稳定的缺点,文中提出ADE-Tri-training(Tri-training with Adaptive Data Editing)新算法.它不仅利用Remove Only剪辑操作对每次迭代可能产生的误标记样例识别并移除,更重要的是采用自适应策略来确定Remove Only触发与抑制的恰当时机.文中证明,PAC理论下自适应策略中一系列判别充分条件可同时确保新训练集规模迭代增大和新假设分类错误率迭代降低更多.UCI数据集上实验结果表明:ADE-Tri-training具有更好的分类泛化性能和健壮性.展开更多
由于光照变化、视角差异、相机抖动和部分遮挡等因素的影响,鲁棒的目标跟踪仍然是计算机视觉领域极具挑战性的研究课题.受协同训练和粒子滤波算法的启发,提出一种快速的自适应目标跟踪方法.该方法采用HOG(histogram of oriented gradien...由于光照变化、视角差异、相机抖动和部分遮挡等因素的影响,鲁棒的目标跟踪仍然是计算机视觉领域极具挑战性的研究课题.受协同训练和粒子滤波算法的启发,提出一种快速的自适应目标跟踪方法.该方法采用HOG(histogram of oriented gradients)和LBP(local binary pattern)描述目标特征并建立分类器,通过协同训练实现分类器的在线更新,有效解决了误差累积问题.为缩小目标搜索的状态空间,利用ICONDENSATION的运动模型和重要采样提高粒子采样的准确性和效率,并引入校正因子抑制虚假目标的干扰,从而提升了跟踪算法的鲁棒性和分类器更新的准确性.在两组标准测试集和两组自建测试集上的对比实验结果验证了所提出跟踪算法的有效性.与基于全局搜索的跟踪方法相比,该算法在不降低跟踪性能的前提下将处理速度提高25倍以上.展开更多
In engineering application,there is only one adaptive weights estimated by most of traditional early warning radars for adaptive interference suppression in a pulse reputation interval(PRI).Therefore,if the training s...In engineering application,there is only one adaptive weights estimated by most of traditional early warning radars for adaptive interference suppression in a pulse reputation interval(PRI).Therefore,if the training samples used to calculate the weight vector does not contain the jamming,then the jamming cannot be removed by adaptive spatial filtering.If the weight vector is constantly updated in the range dimension,the training data may contain target echo signals,resulting in signal cancellation effect.To cope with the situation that the training samples are contaminated by target signal,an iterative training sample selection method based on non-homogeneous detector(NHD)is proposed in this paper for updating the weight vector in entire range dimension.The principle is presented,and the validity is proven by simulation results.展开更多
文摘Tri-training能有效利用无标记样例提高泛化能力.针对Tri-training迭代中无标记样例常被错误标记而形成训练集噪声,导致性能不稳定的缺点,文中提出ADE-Tri-training(Tri-training with Adaptive Data Editing)新算法.它不仅利用Remove Only剪辑操作对每次迭代可能产生的误标记样例识别并移除,更重要的是采用自适应策略来确定Remove Only触发与抑制的恰当时机.文中证明,PAC理论下自适应策略中一系列判别充分条件可同时确保新训练集规模迭代增大和新假设分类错误率迭代降低更多.UCI数据集上实验结果表明:ADE-Tri-training具有更好的分类泛化性能和健壮性.
基金supported by the National Natural Science Foundation of China(62371049)。
文摘In engineering application,there is only one adaptive weights estimated by most of traditional early warning radars for adaptive interference suppression in a pulse reputation interval(PRI).Therefore,if the training samples used to calculate the weight vector does not contain the jamming,then the jamming cannot be removed by adaptive spatial filtering.If the weight vector is constantly updated in the range dimension,the training data may contain target echo signals,resulting in signal cancellation effect.To cope with the situation that the training samples are contaminated by target signal,an iterative training sample selection method based on non-homogeneous detector(NHD)is proposed in this paper for updating the weight vector in entire range dimension.The principle is presented,and the validity is proven by simulation results.
