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Article
Cost Complexity of Proactive Learning via a Reduction to Realizable Active Learning
Institute for Software Research
  • Liu Yang, Carnegie Mellon University
  • Jaime G. Carbonell, Carnegie Mellon University
Date of Original Version
11-1-2009
Type
Technical Report
Rights Management
All Rights Reserved
Abstract or Description
Proactive Learning is a generalized form of active learning with multiple oracles exhibiting different reliabilities (label noise) and costs. We propose a general approach for Proactive Learning that explicitly addresses the cost vs. reliability tradeoff for oracle and instance selection. We formulate the problem in the PAC learning framework with bounded noise, and transform it into realizable active learning via a reduction technique, while keeping the overall query cost small. We propose two types of sequential hypothesis tests (denoted as SeqHT) that estimate the label of a given query from the noisy replies of different oracles with varying reliabilities and costs. We prove correctness and derive cost complexity of the proposed algorithms.
Citation Information
Liu Yang and Jaime G. Carbonell. "Cost Complexity of Proactive Learning via a Reduction to Realizable Active Learning" (2009)
Available at: http://works.bepress.com/jaime_carbonell/176/