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the existence of uniform sample-complexity bounds. A matching lower bound is given for the finite case. 1. Introduction. Reinforcement Learning (RL) is the task 13 Jun 2016 In this work, we provide the first sample complexity analysis for the standard hierarchy of deterministic combinatorial auction classes used in The sample complexity of a machine learning algorithm represents the number of training-samples that it needs in order to successfully learn a target function. The True Sample Complexity of Active Learning. Maria-Florina Balcan. Computer Science Department. Carnegie Mellon University ninamf@cs.cmu.edu. The Optimal Sample Complexity of PAC Learning. Steve Hanneke steve.hanneke@gmail.com. Editor: John Shawe-Taylor. Abstract. This work establishes a new page. Mehryar Mohri - Foundations of Machine Learning. This lecture. PAC Model. Sample complexity, finite H, consistent case. Sample complexity, finite H, 2 Jul 2015 The bound matches known lower bounds up to numerical constant factors. This solves a long-standing open problem on the sample complexity 4 Jul 2016 examples are necessary and sufficient for a learner to output, with probability , a hypothesis that is -close to the target concept . In the related summarizes recent sample complexity results in the reinforcement learning Their sample complexity bounds have no dependence on the size of the state 23 Feb 2015
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