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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
10 relations
Empirical risk minimization
No free lunch in search and optimization
Online machine learning
Probably approximately correct learning
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Broader (1)
Machine learning
Papers overview
Semantic Scholar uses AI to extract papers important to this topic.
2019
2019
Information Source Detection with Limited Time Knowledge
Xuecheng Liu
,
Luoyi Fu
,
Bo Jiang
,
Xiaojun Lin
,
Xinbing Wang
ACM Interational Symposium on Mobile Ad Hoc…
2019
Corpus ID: 170078850
We study the source detection problem using limited timestamps on a given network. Due to the NP-completeness of the maximum…
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Review
2014
Review
2014
Policy gradient approaches for multi-objective sequential decision making: A comparison
Simone Parisi
,
Matteo Pirotta
,
Nicola Smacchia
,
L. Bascetta
,
Marcello Restelli
IEEE Symposium on Adaptive Dynamic Programming…
2014
Corpus ID: 2016342
This paper investigates the use of policy gradient techniques to approximate the Pareto frontier in Multi-Objective Markov…
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2012
2012
Distributed Parameter Estimation via Pseudo-likelihood
Qiang Liu
,
A. Ihler
International Conference on Machine Learning
2012
Corpus ID: 1219545
Estimating statistical models within sensor networks requires distributed algorithms, in which both data and computation are…
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2012
2012
Sample Complexity of Composite Likelihood
Joseph K. Bradley
,
Carlos Guestrin
International Conference on Artificial…
2012
Corpus ID: 2997838
We present the first PAC bounds for learning parameters of Conditional Random Fields [12] with general structures over discrete…
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2012
2012
Learning Partially Observable Models Using Temporally Abstract Decision Trees
Erik Talvitie
Neural Information Processing Systems
2012
Corpus ID: 13444547
This paper introduces timeline trees, which are partial models of partially observable environments. Timeline trees are given…
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2010
2010
Counting Rankings
Jason Riggle
2010
Corpus ID: 16436419
In this paper, I present a recursive algorithm that calculates the number of rankings that are consistent with a set of data (i.e…
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2007
2007
A Queuing-Theoretic Approach to Task Scheduling and Processor Selection for Video-Decoding Applications
Nicholas Mastronarde
,
M. Schaar
IEEE transactions on multimedia
2007
Corpus ID: 974574
We propose a cross-layer design for resource-constrained systems that simultaneously decode multiple video streams on multiple…
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2004
2004
Separating Structure from Interestingness
Taneli Mielikäinen
Pacific-Asia Conference on Knowledge Discovery…
2004
Corpus ID: 12729936
Condensed representations of pattern collections have been recognized to be important building blocks of inductive databases, a…
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Highly Cited
1992
Highly Cited
1992
Algorithms for Identifying Relevant Features
H. Almuallim
,
Thomas G. Dietterich
,
Dearborn
,
HallDepartment
1992
Corpus ID: 16073575
This paper describes eecient methods for exact and approximate implementation of the MIN-FEATURES bias, which prefers consistent…
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1991
1991
Grammatically Biased Learning: Learning Horn Theories Using an Explicit Antecedent Description Langu
William W. Cohen
1991
Corpus ID: 16274968
Every concept learning system produces hypotheses that are written in some sort of constrained language called the concept…
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