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We present a novel classification algorithm for learning with test time budgets. In this setting, the goal is to reduce feature acquisition cost while maintaining classification accuracy. For every decision, our approach dynamically selects features based on previously observed information. Once a desired confidence of a decision is achieved, the acquisition stops and the test instance is classified...
We present a novel convex formulation to learning binary, 2-region local linear classifiers. From this convex formulation, we formulate an online optimization scheme using stochastic gradient descent that allows for efficient training using streaming training data. We demonstrate the fast convergence and accurate classification on the canonical XOR dataset.
In many classification systems, sensing modalities have different acquisition costs. It is often unnecessary to use every modality to classify a majority of examples. We study a multi-stage system in a prediction time cost reduction setting, where the full data is available for training, but for a test example, measurements in a new modality can be acquired at each stage for an additional cost. We...
In many classification systems, features have different acquisition costs. It is often unnecessary to acquire every feature to classify a majority of examples. We study a two-stage system, where new features can be acquired at the second stage for an additional cost. We seek decision rules to reduce the average cost of classifying samples but with little performance degradation. We formulate a two-stage...
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