An active learning approach with uncertainty, representativeness, and diversity.
An active learning approach with uncertainty, representativeness, and diversity.
复制标题
具有不确定性、代表性和多样性的主动学习方法。
DOI：
10.1155/2014/827586
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发表时间：
2014
影响因子：
--
通讯作者：
Cui Z
中科院分区：
文献类型：
--
作者：
He T;Zhang S;Xin J;Zhao P;Wu J;Xian X;Li C;Cui Z
来自物联网的大数据可能会给数据分类带来巨大挑战。大多数主动学习方法选择不确定的或有代表性的未标记实例来查询它们的标签。虽然已经提出了几种主动学习算法来联合收割机的两个标准的查询选择，他们通常是特设的，在寻找未标记的实例，既有信息和代表性，并没有考虑到的多样性的实例。我们通过提出一个新的主动学习框架来应对这一挑战，该框架考虑了不确定性，代表性和多样性。所提出的方法提供了一个系统的方法来测量和结合的不确定性，代表性和多样性的一个例子。首先，利用实例的不确定性和代表性来构造最大信息集。然后，使用核k-means聚类算法来过滤冗余样本，并查询得到的样本的标签。大量的实验结果表明，该方法优于几个国家的最先进的主动学习方法。
Big data from the Internet of Things may create big challenge for data classification. Most active learning approaches select either uncertain or representative unlabeled instances to query their labels. Although several active learning algorithms have been proposed to combine the two criteria for query selection, they are usually ad hoc in finding unlabeled instances that are both informative and representative and fail to take the diversity of instances into account. We address this challenge by presenting a new active learning framework which considers uncertainty, representativeness, and diversity creation. The proposed approach provides a systematic way for measuring and combining the uncertainty, representativeness, and diversity of an instance. Firstly, use instances' uncertainty and representativeness to constitute the most informative set. Then, use the kernel k-means clustering algorithm to filter the redundant samples and the resulting samples are queried for labels. Extensive experimental results show that the proposed approach outperforms several state-of-the-art active learning approaches.
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影响因子：
7.3
作者：
Zhang, C;Chen, TS
通讯作者：
Chen, TS
DOI：
10.1109/jstsp.2011.2139193
发表时间：
2011-06-01
影响因子：
7.5
作者：
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通讯作者：
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影响因子：
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作者：
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通讯作者：
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DOI：
10.1109/tpami.2011.158
发表时间：
2012-03-01
影响因子：
23.6
作者：
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通讯作者：
Ferrari, Vittorio