Learning from Cluster Examples-Employing Attributes of Clusters.
Learning from Cluster Examples-Employing Attributes of Clusters.
复制标题
从集群示例中学习-利用集群的属性。
DOI：
10.1527/tjsai.18.86
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发表时间：
2003
影响因子：
--
通讯作者：
F. Motoyoshi
中科院分区：
文献类型：
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作者：
Toshihiro Kamishima;S. Akaho;F. Motoyoshi
从聚类示例中学习（LCE）是两个常见分类任务的复合任务：从示例中学习和聚类。从聚类示例中学习涉及尝试获取可用于从给定示例中划分未知对象集的规则。在难以形式化派生分区的显式算法，而易于指定正确分区的单个示例的任何情况下，学习此类分区规则的通用方法都是有用的。为了提高LCE的估计精度，本文利用聚类的属性，提出了一种处理这类属性的方法。通过将这种方法应用于人工数据，我们展示了性能的改进。
Learning from cluster examples (LCE) is a composite task of two common classification tasks: learning from examples and clustering. Learning from cluster examples involves an attempt to acquire a rule that can be used to partition an unseen object set from given examples. A general method for learning such partitioning rules is useful in any situation where explicit algorithms for deriving partitions are hard to formalize, while individual examples of correct partitions are easy to specify. In this paper, to improve estimation accuracy of LCE, we employ attributes of clusters and propose a method that can handle this type of attributes. We show improvements of performance by applying this method to artificial data.