Incremental Learning Method of GRBF with Recalling of Interfered Patterns - Application for Case Based Reasoning Systems
Incremental Learning Method of GRBF with Recalling of Interfered Patterns - Application for Case Based Reasoning Systems
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具有干扰模式回忆的 GRBF 增量学习方法 - 基于案例的推理系统的应用
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发表时间：
1997
期刊：
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通讯作者：
N. Ishii
中科院分区：
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作者：
K. Yamauchi;N. Yamaguchi;N. Ishii
提出了一种基于案例推理（CBR）系统的广义径向基函数（GRBF）低成本增量学习方法。CBR系统是一种使用过去案例解决新问题的推理系统。为了实现推理，系统必须从案例库中查找与新问题相似的过去案例。如果发现这种情况，系统必须对其进行调整以测试新问题。适应的情况下是所提出的解决方案。如果解决方案不好，则由专家进行修改，专家给出正确的解决方案并将其存储到案例数据库中作为新案例。如果一个系统使用神经网络作为它的案例数据库，搜索过程和适应过程是不需要的。系统只需将新问题提交给神经网络。然后，一个合适的解决方案出现在神经网络的输出层。神经网络必须在新案例出现时完全学习新案例。然而，如果网络仅仅通过参考它们来学习新的案例，那么网络可能会忘记旧的记忆案例。避免记忆丢失的一种方法是用所有记忆的案例学习新的案例。然而，它需要很高的计算能力。为了解决这个问题，我们提出了一种基于记忆干扰模式的广义径向基函数（GRBF）增量学习方法。Poggio等人，1990年]。在ILRI中，GRBF通过重新学习被增量学习干扰的少数回忆过去的案例来学习新案例。
This paper proposes a low-cost incremental learning method of Generalized Radial Basis Function (GRBF) for a Case Based Reasoning (CBR) system. A CBR system is one type of reasoning system that uses past cases for solving new problems. To realize the reasoning, the system has to search a past case which is similar to the new problem from a case database. If such case is found, the system has to adapt it so as to t the new problem. The adapted case is the presented solution. If the solution is not good, it is revised by an expert who gives a correct solution and stores it into the case database as a new case. If a system uses a neural network as its case database, the searching process and the adaptation process are not needed. The system only has to present the new problem to the neural network. Then, an appropriate solution appears in the output layer of the neural network. The neural network has to learn new cases completely when the case appears. However, if the network learns the new cases only by referring to them, the network probably forget old memorized cases. A certain way to avoid the lost of memory is learning the new cases with all memorized cases. It needs, however, a high computational power. To solve this problem, we propose an Incremental Learning method with Recalling Interfered patterns (ILRI) for Generalized Radial Basis Function (GRBF) [T. Poggio et al.,1990]. In ILRI, the GRBF learns new cases with relearning of the few recalled past cases that are interfered with the incremental learning.