document: Designing efficient cascaded classifiers : tradeoff between accuracy and cost We propose a method to train a cascade of classifiers by simultaneously optimizing all its stages . The approach relies on the idea of optimizing soft cascades . In particular , instead of optimizing a deterministic hard cascade , we optimize a stochastic soft cascade where each stage accepts or rejects samples according to a probability distribution induced by the previous stage-specific classifier . The overall system accuracy is maximized while explicitly controlling the expected cost for feature acquisition . Experimental results on three clinically relevant problems show the effectiveness of our proposed approach in achieving the desired tradeoff between accuracy and feature acquisition cost .
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