Incremental learning from noisy data
Incremental learning from noisy data
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DOI：
10.1007/bf00116895
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发表时间：
2004
期刊：
影响因子：
7.5
通讯作者：
J. C. Schlimmer;R. Granger
中科院分区：
文献类型：
--
作者：
J. C. Schlimmer;R. Granger
在给定噪声实例的情况下，概念描述的归纳是困难的，并且当概念可能随时间改变时进一步加剧。本文提出了一个解决方案，已被心理学和数学结果的指导。该方法是基于一个分布式的概念描述，这是由一组加权，符号表征。两个学习过程逐渐修改了这一描述。一个是调整特征权重，另一个是创建新的特征。后一个过程中描述的搜索通过空间的可能性，并示出需要线性空间相对于描述语言中的属性值对的数量。该方法通过将每个学习概念的属性添加到实例描述中来在后续学习中利用先前获取的概念定义。一个名为STAGGER的程序充分体现了这种方法，本文报告了一些实证分析其性能。由于理解一种新的学习方法和现有的学习方法之间的关系可能很困难，本文首先回顾了一个讨论机器学习系统的框架，然后在该框架中描述了STAGGER。
Induction of a concept description given noisy instances is difficult and is further exacerbated when the concepts may change over time. This paper presents a solution which has been guided by psychological and mathematical results. The method is based on a distributed concept description which is composed of a set of weighted, symbolic characterizations. Two learning processes incrementally modify this description. One adjusts the characterization weights and another creates new characterizations. The latter process is described in terms of a search through the space of possibilities and is shown to require linear space with respect to the number of attribute-value pairs in the description language. The method utilizes previously acquired concept definitions in subsequent learning by adding an attribute for each learned concept to instance descriptions. A program called STAGGER fully embodies this method, and this paper reports on a number of empirical analyses of its performance. Since understanding the relationships between a new learning method and existing ones can be difficult, this paper first reviews a framework for discussing machine learning systems and then describes STAGGER in that framework.