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Action-reward learning is a reinforcement learning method. In this machine learning approach, an agent interacts with non-deterministic control domain. The agent selects actions at decision epochs and the control domain gives rise to rewards with which the performance measures of the actions are...
With the rapid development of case-based reasoning (CBR) techniques such as case retrieval and case adaptation, CBR has been widely applied to various real-world applications. A successful case-based reasoning system requires a high-quality case base, which provides rich and efficient solutions...
This paper presents a framework for incremental neural learning (INL) that allows a base neural learning system to incrementally learn new knowledge from only new data without forgetting the existing knowledge. Upon subsequent encounters of new data examples, INL utilizes prior knowledge to...
In this paper we investigate the combined effects of quantization and clipping on high-order function neural networks (HOFNN). Statistical models are used to analyze the effects of quantization in a digital implementation. We analyze the performance degradation caused as a function of the number...
In this paper, we present a new method for dealing with feature subset selection based on fuzzy entropy measures for handling classification problems. First, we discretize numeric features to construct the membership function of each fuzzy set of a feature. Then, we select the feature subset...
Simulated Annealing (SA) is a popular global minimization method. Two weaknesses are associated with standard SA: firstly, the search process is memory-less and therefore can not avoid revisiting regions that are less likely to contain global minimum; and secondly the randomness in generating a...
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