منابع مشابه
Agnostic Active Learning Without Constraints
We present and analyze an agnostic active learning algorithm that works withoutkeeping a version space. This is unlike all previous approaches where a restrictedset of candidate hypotheses is maintained throughout learning, and only hypothe-ses from this set are ever returned. By avoiding this version space approach, ouralgorithm sheds the computational burden and brittleness as...
متن کاملAgnostic Active Learning Without Constraints
We present and analyze an agnostic active learning algorithm that works without keeping a version space. This is unlike all previous approaches where a restricted set of candidate hypotheses is maintained throughout learning, and only hypotheses from this set are ever returned. By avoiding this version space approach, our algorithm sheds the computational burden and brittleness associated with ...
متن کاملEfficient and Parsimonious Agnostic Active Learning
We develop a new active learning algorithm for the streaming setting satisfying three important properties: 1) It provably works for any classifier representation and classification problem including those with severe noise. 2) It is efficiently implementable with an ERM oracle. 3) It is more aggressive than all previous approaches satisfying 1 and 2. To do this we create an algorithm based on ...
متن کاملA General Agnostic Active Learning Algorithm
We present a simple, agnostic active learning algorithm that works for any hypothesis class of bounded VC dimension, and any data distribution. Our algorithm extends a scheme of Cohn, Atlas, and Ladner to the agnostic setting, by (1) reformulating it using a reduction to supervised learning and (2) showing how to apply generalization bounds even for the non-i.i.d. samples that result from selec...
متن کاملBeyond Disagreement-Based Agnostic Active Learning
We study agnostic active learning, where the goal is to learn a classifier in a pre-specified hypothesis class interactively with as few label queries as possible, while making no assumptions on the true function generating the labels. The main algorithms for this problem are disagreement-based active learning, which has a high label requirement, and margin-based active learning, which only app...
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ژورنال
عنوان ژورنال: Journal of Computer and System Sciences
سال: 2009
ISSN: 0022-0000
DOI: 10.1016/j.jcss.2008.07.003