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Sample complexity

The sample complexity of a machine learning algorithm represents the number of training-samples that it needs in order to successfully learn a target… 
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Related topics

Related topics

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Empirical risk minimizationNo free lunch in search and optimizationOnline machine learningProbably approximately correct learning

Broader (1)

Machine learning

Papers overview

Semantic Scholar uses AI to extract papers important to this topic.
Review
2014
Review
2014
Policy gradient approaches for multi-objective sequential decision making: A comparison
This paper investigates the use of policy gradient techniques to approximate the Pareto frontier in Multi-Objective Markov… 
2014
2014
A Compositional Model for Low-Dimensional Image Set Representation
Learning a low-dimensional representation of images is useful for various applications in graphics and computer vision. Existing… 
2012
2012
Distributed Parameter Estimation via Pseudo-likelihood
Estimating statistical models within sensor networks requires distributed algorithms, in which both data and computation are… 
2012
2012
Sample Complexity of Composite Likelihood
We present the first PAC bounds for learning parameters of Conditional Random Fields [12] with general structures over discrete… 
2010
2010
Counting Rankings
In this paper, I present a recursive algorithm that calculates the number of rankings that are consistent with a set of data (i.e… 
2008
2008
Efficient reinforcement learning in parameterized models: discrete parameters
We consider reinforcement learning in the parameterized setup, where the model is known to belong to a finite set of Markov… 
2007
2007
A Queuing-Theoretic Approach to Task Scheduling and Processor Selection for Video-Decoding Applications
We propose a cross-layer design for resource-constrained systems that simultaneously decode multiple video streams on multiple… 
2004
2004
Separating Structure from Interestingness
Condensed representations of pattern collections have been recognized to be important building blocks of inductive databases, a… 
Highly Cited
1992
Highly Cited
1992
Algorithms for Identifying Relevant Features
This paper describes eecient methods for exact and approximate implementation of the MIN-FEATURES bias, which prefers consistent… 
1991
1991
Grammatically Biased Learning: Learning Horn Theories Using an Explicit Antecedent Description Langu
Every concept learning system produces hypotheses that are written in some sort of constrained language called the concept… 
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