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Approximation error

Known as: Percentage error, Absolute Uncertainty, Percent deviation 
The approximation error in some data is the discrepancy between an exact value and some approximation to it. An approximation error can occur because… 
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Related topics

Related topics

22 relations
AlgorithmApproximationApproximation theoryBelief propagation

Papers overview

Semantic Scholar uses AI to extract papers important to this topic.
Highly Cited
2018
Highly Cited
2018
Addressing Function Approximation Error in Actor-Critic Methods
In value-based reinforcement learning methods such as deep Q-learning, function approximation errors are known to lead to… 
Highly Cited
2015
Highly Cited
2015
Deep Reinforcement Learning with Double Q-Learning
The popular Q-learning algorithm is known to overestimate action values under certain conditions. It was not previously known… 
Highly Cited
2006
Highly Cited
2006
Improved Approximation Algorithms for Large Matrices via Random Projections
  • Tamás Sarlós
  • IEEE Annual Symposium on Foundations of Computer…
  • 2006
  • Corpus ID: 1299951
Several results appeared that show significant reduction in time for matrix multiplication, singular value decomposition as well… 
Highly Cited
2003
Highly Cited
2003
ESTIMATING THE APPROXIMATION ERROR IN LEARNING THEORY
Let B be a Banach space and (ℋ,‖·‖ℋ) be a dense, imbedded subspace. For a ∈ B, its distance to the ball of ℋ with radius R… 
Highly Cited
2001
Highly Cited
2001
Greedy function approximation: A gradient boosting machine.
Function estimation/approximation is viewed from the perspective of numerical optimization in function space, rather than… 
Highly Cited
1998
Highly Cited
1998
Metro: Measuring Error on Simplified Surfaces
This paper presents a new tool, Metro, designed to compensate for a deficiency in many simplification methods proposed in… 
Highly Cited
1997
Highly Cited
1997
Wrappers for Feature Subset Selection
Highly Cited
1997
Highly Cited
1997
Generalized Gradient Approximation Made Simple [Phys. Rev. Lett. 77, 3865 (1996)]
For the molecules Be2, F2, and P2 of Table I, the unrestricted Hartree-Fock solution breaks the singlet spin symmetry, even… 
Highly Cited
1996
Highly Cited
1996
Analysis of Temporal-Diffference Learning with Function Approximation
We present new results about the temporal-difference learning algorithm, as applied to approximating the cost-to-go function of a… 
Highly Cited
1993
Highly Cited
1993
Universal approximation bounds for superpositions of a sigmoidal function
  • A. Barron
  • IEEE Transactions on Information Theory
  • 1993
  • Corpus ID: 15383918
Approximation properties of a class of artificial neural networks are established. It is shown that feedforward networks with one… 
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