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Researchers have embraced a variety of machine learning (ML) techniques in their efforts to improve the quality of learning programs. The recent evolution of hybrid architectures for machine learning systems has resulted in several approaches that combine rule-induction methods with case-based reasoning techniques to engender performance improvements over more-traditional one-representation architectures. We briefly survey several major rule-induction and case-based reasoning ML systems. We then examine some interesting hybrid combinations of these systems, and explain their strengths and weaknesses as learning systems. We present a balanced approach to constructing a hybrid architecture, along with arguments in favor of this balance and mechanisms for achieving a proper balance. Finally, we present some initial empirical results from testing our ideas and draw some conclusions based on those results.