Skip to search formSkip to main contentSkip to account menu
You are currently offline. Some features of the site may not work correctly.

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… Expand
Wikipedia

Papers overview

Semantic Scholar uses AI to extract papers important to this topic.
Highly Cited
2018
Highly Cited
2018
In value-based reinforcement learning methods such as deep Q-learning, function approximation errors are known to lead to… Expand
  • figure 1
  • figure 2
  • figure 3
  • figure 4
  • figure 5
Highly Cited
2007
Highly Cited
2007
One of the major settings of global sensitivity analysis is that of fixing non-influential factors, in order to reduce the… Expand
Highly Cited
2006
Highly Cited
2006
  • Tamás Sarlós
  • 47th Annual IEEE Symposium on Foundations of…
  • 2006
  • Corpus ID: 1299951
Several results appeared that show significant reduction in time for matrix multiplication, singular value decomposition as well… Expand
Highly Cited
2003
Highly Cited
2003
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… Expand
Highly Cited
2001
Highly Cited
2001
Function estimation/approximation is viewed from the perspective of numerical optimization in function space, rather than… Expand
  • figure 1
  • figure 2
  • figure 3
  • figure 4
  • figure 5
Highly Cited
2000
Highly Cited
2000
This monograph presents a summary account of the subject of a posteriori error estimation for finite element approximations of… Expand
  • figure 1
  • figure 2
  • figure 3
  • figure 4
  • table 1
Highly Cited
1998
Highly Cited
1998
This paper presents a new tool, Metro, designed to compensate for a deficiency in many simplification methods proposed in… Expand
Highly Cited
1997
Highly Cited
1997
Abstract In the feature subset selection problem, a learning algorithm is faced with the problem of selecting a relevant subset… Expand
  • figure I
  • table 1
  • figure 2
  • figure 3
  • figure 4
Highly Cited
1996
Highly Cited
1996
We present new results about the temporal-difference learning algorithm, as applied to approximating the cost-to-go function of a… Expand
  • figure 1
Highly Cited
1993
Highly Cited
1993
  • A. Barron
  • IEEE Trans. Inf. Theory
  • 1993
  • Corpus ID: 15383918
Approximation properties of a class of artificial neural networks are established. It is shown that feedforward networks with one… Expand