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VC dimension

Known as: VCD (disambiguation), VC, VC-dimension 
In statistical learning theory and computational learning theory, the VC dimension (for Vapnik–Chervonenkis dimension) is a measure of the capacity… 
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Related topics

Related topics

29 relations
Alexey ChervonenkisAlgorithmAlgorithmic inferenceArt gallery problem

Broader (2)

Computational learning theoryStatistical classification

Papers overview

Semantic Scholar uses AI to extract papers important to this topic.
Highly Cited
2017
Highly Cited
2017
Nearly-tight VC-dimension bounds for piecewise linear neural networks
We prove new upper and lower bounds on the VC-dimension of deep neural networks with the ReLU activation function. These bounds… 
2017
2017
Erdős–Hajnal Conjecture for Graphs with Bounded VC-Dimension
The Vapnik–Chervonenkis dimension (in short, VC-dimension) of a graph is defined as the VC-dimension of the set system induced by… 
Highly Cited
2017
Highly Cited
2017
Voice Conversion from Unaligned Corpora Using Variational Autoencoding Wasserstein Generative Adversarial Networks
Building a voice conversion (VC) system from non-parallel speech corpora is challenging but highly valuable in real application… 
Highly Cited
2014
Highly Cited
2014
Recursive teaching dimension, VC-dimension and sample compression
This paper is concerned with various combinatorial parameters of classes that can be learned from a small set of examples. We… 
Highly Cited
2010
Highly Cited
2010
From Secrecy to Soundness: Efficient Verification via Secure Computation
We study the problem of verifiable computation (VC) in which a computationally weak client wishes to delegate the computation of… 
Highly Cited
2010
Highly Cited
2010
Recent developments in standardization of high efficiency video coding (HEVC)
This paper reports on recent developments in video coding standardization, particularly focusing on the Call for Proposals (CfP… 
Highly Cited
2001
Highly Cited
2001
Automatic Deductive Verification with Invisible Invariants
The paper presents a method for the automatic verification of a certain class of parameterized systems. These are bounded-data… 
Highly Cited
2000
Highly Cited
2000
Coefficient of determination in nonlinear signal processing
Highly Cited
1997
Highly Cited
1997
Polynomial Bounds for VC Dimension of Sigmoidal and General Pfaffian Neural Networks
We introduce a new method for proving explicit upper bounds on the VC dimension of general functional basis networks and prove as… 
Highly Cited
1992
Highly Cited
1992
Automatic Capacity Tuning of Very Large VC-Dimension Classifiers
Large VC-dimension classifiers can learn difficult tasks, but are usually impractical because they generalize well only if they… 
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