Abstract
Applying machine learning in Electronic Design Automation (EDA) has received growing interests in recent years. One approach to analyze data in EDA applications can be called feature-based analytics. In this context, the paper explains the inadequacy of adopting a traditional machine learning problem formulation view. Then, an alternative machine learning view is suggested where learning from data is treated as an iterative search process. The theoretical and practical considerations for implementing such a search process are discussed in the context of various applications.
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