Learning by switching generation and reasoning methods in several knowledge representations towards the simulation of human learning process
Learning by switching generation and reasoning methods in several knowledge representations towards the simulation of human learning process
复制标题
通过切换多种知识表示中的生成和推理方法来模拟人类学习过程进行学习
DOI：
10.1109/fuzz.2002.1005097
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发表时间：
2002
期刊：
影响因子：
--
通讯作者：
Kazuhisa Seta
中科院分区：
文献类型：
--
作者：
M. Umano;Yuji Matsumoto;Yushi Uno;Kazuhisa Seta
我们在解决问题时，首先是没有知识，然后通过观察新的数据逐渐获得一些知识，最后得到解决问题的完整知识。在第一阶段，我们有一个简单形式的具体知识，在最后阶段，我们有一个复杂形式的一般知识。为了模拟这种学习机制，我们必须在几个阶段中将联合收割机几种学习方法结合起来。提出了一种在不同知识表示下进行规则重构和推理方法切换的方法，以及在多种知识表示下进行规则生成方法切换的方法。我们模拟的方法应用于虹膜分类问题。
When we solve a problem, we firstly have no knowledge and gradually acquire some piece of knowledge by observing new data, and at last arrive at complete knowledge for solving the problem. We have a simple form of specific knowledge in the first stage and a complex form of a general one in the final stage. To simulate this kind of learning mechanism, we must combine several kinds of learning methods in several stages. We proposed a method of not only reconstructing rules and switching reasoning methods in each knowledge representation but also switching rule generation methods in several knowledge representation. We simulated the method by applying to the iris classification problem.