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Review

2018

Review

2018

An Operational Land Surface Temperature Product for Landsat Thermal Data: Methodology and Validation

Thermal sensors onboard Landsat satellites have been underutilized due to the lack of consistent and accurate methodologies for… Expand

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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

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Highly Cited

2008

Highly Cited

2008

The typical paradigm for obtaining a compressed version of a discrete signal represented by a vector x ∈ R is to choose an… Expand

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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

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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

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Highly Cited

2001

Highly Cited

2001

Function estimation/approximation is viewed from the perspective of numerical optimization in function space, rather than… Expand

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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

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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

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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

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Highly Cited

1993

Highly Cited

1993

Approximation properties of a class of artificial neural networks are established. It is shown that feedforward networks with one… Expand

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