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Decentralized optimization algorithms save remarkable communication overheads in distributed deep learning since each node averages locally with neighbors. The network topology connecting all nodes de...
Imagine sitting on a park bench, watching someone stroll by. While the scene may constantly change as the person walks, the human brain can transform that dynamic visual information into a more stable...
A team of researchers from the University of California, Berkeley, the University of California, Davis and the Texas Advanced Computing Center (TACC) published the results of an effort to harness the ...
The synergy of education and technology can transform the world we live in. A virtual experience is usually achieved by feeding sensory information to a user via virtual displays (head mounted display...
All individuals are unique but millions of people share names. How to distinguish -- or as it is technically known, disambiguate -- people with common names and determine which John Smith or Maria Gar...
Remote Sensing Technology Center of Japan (RESTEC) has been organizing various kinds of remote sensing training programs since RESTEC was established in 1975. Among them, remote sensing training cou...
In data-mining applications, we are frequently faced with a large fraction of missing entries in the data matrix, which is problematic for most discriminant machine learning algorithms. A solution tha...
We propose Maximum Ranking Correlation (MRC) as an objective function in discrimi-native tuning of parameters in a linear model of Statistical Machine Translation (SMT). We try to maximize the ranking...
This article discusses the lessons learned from developing and delivering the Vocational Management Training for the European Tourism Industry (VocMat) online training programme, which was aimed at pr...
针对中文组织机构名识别中的标注语料匮乏问题,提出了一种基于协同训练机制的组织机构名识别方法。该算法利用Tri-training学习方式将基于条件随机场的分类器、基于支持向量机的分类器和基于记忆学习方法的分类器组合成一个分类体系,并依据最优效用选择策略进行新加入样本的选择。在大规模真实语料上与co-training方法进行了比较实验,实验结果表明,此方法能有效利用大量未标注语料提高算法的泛化能力。 ...
In this work, we present a novel method to detect violent shots in movies. The detection process is split into two views–––the audio and video views. From the audio-view, a weakly-supervised method is...
The performance of a learning-based method highly depends on the quality of a training set. However, it is very challenging to collect an efficient and effective training set for training a good class...
当前机器学习面临的主要问题之一是如何有效地处理海量数据,而标记训练数据是十分有限且不易获得的。提出了一种新的半监督SVM算法,该算法在对SVM训练中,只要求少量的标记数据,并能利用大量的未标记数据对分类器反复的修正。在实验中发现,Tri-training的应用确实能够提高SVM算法的分类精度,并且通过增大分类器间的差异性能够获得更好的分类效果,所以Tri-training对分类器的要求十分宽松,通...
在已有的问答模式学习中,模式定义和候选答案评分偏于简单,而且学习过程依赖于人工标定语料。通过挖掘Web文本中动、名词序列的骨架模式,用以扩充模式定义;将self-training学习机制引入问答模式学习:用一对训练语料进行初始学习,通过互联网搜索,自动选择可靠程度较高的问答对,重新训练;扩充了启发规则,改进候选答案的评分方法。实验结果表明:所提出的问答模式学习方法能有效地提高中文问答系统的性能。
运动目标跟踪是计算机视觉的核心问题之一,广泛应用于诸多领域。该文提出一种基于Co-Training半监督学习框架的目标跟踪方法。该方法融合2种互相独立的特征信息来描述目标模型,采用Co-Training来协同更新模型,有效避免了现有方法的误差累积问题。实验结果证明,该方法在复杂场景下仍能实现稳定有效的跟踪。

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