Rule extraction by a structural learning of neural networks
通过神经网络的结构学习进行规则提取
基本信息
- 批准号:07680404
- 负责人:
- 金额:$ 1.6万
- 依托单位:
- 依托单位国家:日本
- 项目类别:Grant-in-Aid for Scientific Research (C)
- 财政年份:1995
- 资助国家:日本
- 起止时间:1995 至 1996
- 项目状态:已结题
- 来源:
- 关键词:
项目摘要
In extracting rules from continuous valued inputs, the balance between mean square output error and the complexity of rules is important. Information criteria such as AIC represents this trade-off. In a structural learning with forgetting (SLF), the amount of forgetting is determined by minimizing AIC.However, SLF alone cannot produce rules of appropriate complexity. To overcome this difficulty, neural networks of various degrees of, complexity are trained. The degree of complexity, here, is defined by the maximum number of incoming connections to each hidden unit. From among these, the one with the smallest AIC is selected as optimal. Since outputs of hidden units are binary owing to the learning with hidden units clarification, incoming connection weights to each hidden unit determine the corresponding discriminating hyperplane. A logical combination of these hyperplanes provides rules. Furthermore, comparison with C4.5 popular in machine learning. Also comparison is made with KT met … More hod proposed by Fu. C4.5 and KT method can only produce rules with only one attribute at each term. On the other hand, the proposed method can produce rules of various complexities.The first task is to divide a two-dimensional plane into two categories. In this case, rules with only one attribute at each term is not a natural representation. C4.5 generates many simple rules, but the proposed method can explain all data by 6 rules with two attributes. The second task is the classification of irises into 3 categories : setosa, versicolor, and virginica. Three rules with at most 3 attributes can explain 148 samples out of 150. The third task is the diagnosis of thyroid functioning into 3 classes : normal, hypo and hyper functioning. In this case 4 rules with at most 2 attributes can explain all 215 samples. Furthermore, the number of classification errors is smaller than those by C4.5 and KT method.Concerning rule extraction from both continuous and discrete inputs and that from continuous inputs and outputs, satisfactory results are not yet obtained due to inherent difficlty. These are left for further study. Less
在从连续值输入中提取规则时,均方输出误差和规则复杂度之间的平衡是很重要的。AIC等信息标准代表了这种权衡。在带遗忘的结构学习(SLF)中,遗忘量由最小化AIC决定,但单靠SLF不能产生适当复杂度的规则。为了克服这个困难,训练了各种复杂程度的神经网络。这里,复杂度由每个隐藏单元的最大传入连接数定义。从这些中,具有最小AIC的一个被选择为最佳。由于隐藏单元的输出是二进制的,由于学习与隐藏单元澄清,传入的连接权重,每个隐藏单元确定相应的判别超平面。这些超平面的逻辑组合提供了规则。此外,与机器学习中流行的C4.5相比。并与KT met进行了比较 ...更多信息 傅立叶提出的方法。C4.5和KT方法只能产生每项只有一个属性的规则。另一方面,所提出的方法可以产生各种复杂度的规则。第一个任务是将一个二维平面分成两类。在这种情况下,每个术语只有一个属性的规则不是自然的表示。C4.5生成了许多简单的规则,但所提出的方法可以用6个规则和两个属性来解释所有数据。第二项任务是将鸢尾花分为3类:刚毛鸢尾、杂色鸢尾和处女鸢尾。三个规则最多3个属性可以解释150个样本中的148个。第三个任务是诊断甲状腺功能分为3类:正常,低和高功能。在这种情况下,最多具有2个属性的4个规则可以解释所有215个样本。对于连续输入和离散输入的规则提取以及连续输入和输出的规则提取,由于其固有的困难,目前还没有得到令人满意的结果。这些问题留待进一步研究。少
项目成果
期刊论文数量(24)
专著数量(0)
科研奖励数量(0)
会议论文数量(0)
专利数量(0)
Masumi Ishikawa: "Structural learning and knowledge acquisition" International Conference on Neural Networks(ICNN'96),Plenary,Panel and Special Sessions. 100-105 (1996)
Masumi Ishikawa:“结构学习和知识获取”国际神经网络会议(ICNN96),全体会议,小组会议和特别会议。
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- 影响因子:0
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Masumi Ishikawa: "Neural networks approach to rule extraction" ANNES'95. 6-9 (1995)
Masumi Ishikawa:“规则提取的神经网络方法”ANNES95。
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Masumi Ishikawa: Structural Learning and Rule Discovery from Data S.Amari and N.Kasabov Eds.Brain-Like Computing and Intelligent Information Systems. Springer, 396-415 (1998)
Masumi Ishikawa:从数据中进行结构学习和规则发现 S.Amari 和 N.Kasabov Eds.类脑计算和智能信息系统。
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Masumi Ishikawa: "Structural learning with forgetting" Neural Networks. 9. 509-521 (1996)
Masumi Ishikawa:“结构性学习与遗忘”神经网络。
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石川眞澄: "ニューラルネットワークによるデータ処理" ぶんせき. 257. 350-355 (1996)
Masumi Ishikawa:“使用神经网络进行数据处理”Bunseki 257. 350-355 (1996)。
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ISHIKAWA Masumi其他文献
ISHIKAWA Masumi的其他文献
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{{ truncateString('ISHIKAWA Masumi', 18)}}的其他基金
Advancement of reinforcement learning and its applications to mobile robots based on spatio-temporal segmentation of the environment
基于环境时空分割的强化学习进展及其在移动机器人中的应用
- 批准号:
18500175 - 财政年份:2006
- 资助金额:
$ 1.6万 - 项目类别:
Grant-in-Aid for Scientific Research (C)
Development of a cognitive map for mobile robot and its advancement inspired by place cells in hippocampus
移动机器人认知地图的开发及其受海马位置细胞启发的进展
- 批准号:
15500140 - 财政年份:2003
- 资助金额:
$ 1.6万 - 项目类别:
Grant-in-Aid for Scientific Research (C)
Self-organization of environmental maps based on scene images and navigation of mobile robots
基于场景图像的环境地图自组织及移动机器人导航
- 批准号:
11680393 - 财政年份:1999
- 资助金额:
$ 1.6万 - 项目类别:
Grant-in-Aid for Scientific Research (C)
Neural network learning with regulatizers and generalization ability
具有调节器和泛化能力的神经网络学习
- 批准号:
09680371 - 财政年份:1997
- 资助金额:
$ 1.6万 - 项目类别:
Grant-in-Aid for Scientific Research (C)
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