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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.
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… 
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
Bisimulation for Markov Decision Processes through Families of Functional Expressions
We transfer a notion of quantitative bisimilarity for labelled Markov processes [1] to Markov decision processes with continuous… 
2012
2012
Distributed Parameter Estimation via Pseudo-likelihood
Estimating statistical models within sensor networks requires distributed algorithms, in which both data and computation are… 
2010
2010
Differential Privacy and the Fat-Shattering Dimension of Linear Queries
In this paper, we consider the task of answering linear queries under the constraint of differential privacy. This is a general… 
2004
2004
A new bound on the generalization rate of sampled convex programs
This paper deals with the sampled scenarios approach to robust convex programming. It has been shown in previous works that by… 
2003
2003
Coverage-Optimized Retrieval
We present a generalization of similarity-based retrieval in recommender systems which ensures that for any case that is… 
1998
1998
Learning Coordination Strategies for Cooperative Multiagent Systems
A central issue in the design of cooperative multiagent systems is how to coordinate the behavior of the agents to meet the goals… 
1992
1992
Case{based Meta Learning: Sustained Learning supported by a Dynamically Biased Version Space
It is well{recognized that in practical inductive learning systems the search for a concept must be heavily biased. In addition… 
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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