On the public citation graph
Bounding the Vapnik-Chervonenkis dimension of concept classes parameterized by real numbers
0 signed · 85 inferred · view as graph
Inferred edges come from two machine sources: sweeps of open repository metadata (mostly SSRN and Zenodo), and a bulk OpenAlex citation snapshot. Every row below is labelled with the one it came from. Neither is a complete citation index — absence here is not evidence of absence.
Is this your work? Sign in with ORCID and put your name on the
claims that are yours.
Reference health. See whether every reference in this work resolves to a
known work and none has been retracted — and get a dated CiteStamped
badge you can embed.
What this work says
Outgoing claims: the papers this work cites, builds on, or argues with.
inferredcites →https://doi.org/10.1016/0012-365x(81)90274-0openalex-snapshot
Some special vapnik-chervonenkis classes
inferredcites →https://doi.org/10.1016/0020-0190(83)90042-xopenalex-snapshot
A linear time algorithm for the Hausdorff distance between convex polygons
inferredcites →https://doi.org/10.1016/0022-0000(92)90039-lopenalex-snapshot
Feedforward nets for interpolation and classification
inferredcites →https://doi.org/10.1016/0196-6774(82)90002-5openalex-snapshot
Lower bounds for algebraic decision trees
inferredcites →https://doi.org/10.1016/0890-5401(89)90002-3openalex-snapshot
A general lower bound on the number of examples needed for learning
inferredcites →https://doi.org/10.1016/0890-5401(91)90058-aopenalex-snapshot
Results on learnability and the Vapnik-Chervonenkis dimension
inferredcites →https://doi.org/10.1016/s0747-7171(10)80003-3openalex-snapshot
On the computational complexity and geometry of the first-order theory of the reals. Part I: Introduction. Preliminaries. The geometry of semi-algebraic sets. The decision problem for the existential theory of the reals
inferredcites →https://doi.org/10.1090/s0002-9939-1964-0161339-9openalex-snapshot
On the Betti Numbers of Real Varieties
inferredcites →https://doi.org/10.1090/s0002-9947-1968-0226281-1openalex-snapshot
Lower Bounds for Approximation by Nonlinear Manifolds
inferredcites →https://doi.org/10.1112/jlms/s2-45.2.377openalex-snapshot
Vapnik-Chervonenkis Classes of Definable Sets
inferredcites →https://doi.org/10.1137/1116025openalex-snapshot
On the Uniform Convergence of Relative Frequencies of Events to Their Probabilities
inferredcites →https://doi.org/10.1137/s0097539793256041openalex-snapshot
Bounds for the Computational Power and Learning Complexity of Analog Neural Nets
inferredcites →https://doi.org/10.1145/167088.167192openalex-snapshot
Finiteness results for sigmoidal “neural” networks
inferredcites →https://doi.org/10.1145/76359.76371openalex-snapshot
Learnability and the Vapnik-Chervonenkis dimension
inferredcites →https://doi.org/10.1145/800061.808735openalex-snapshot
Lower bounds for algebraic computation trees
inferredcites →https://doi.org/10.1162/neco.1989.1.1.151openalex-snapshot
What Size Net Gives Valid Generalization?
inferredcites →https://doi.org/10.1214/aop/1176995384openalex-snapshot
Central Limit Theorems for Empirical Measures
inferredcites →https://doi.org/10.1214/aos/1176347373openalex-snapshot
Some New Vapnik-Chervonenkis Classes
What points at this work
Incoming claims: where this work appears in other papers’ reference lists and arguments.
inferred← citeshttps://doi.org/10.1002/9781119162254.ch06openalex-snapshot
Dynamical Systems and Interaction Networks
inferred← citeshttps://doi.org/10.1006/inco.1996.0065openalex-snapshot
Using Vapnik–Chervonenkis Dimension to Analyze the Testing Complexity of Program Segments
inferred← citeshttps://doi.org/10.1006/inco.1999.2806openalex-snapshot
On the Complexity of Learning for Spiking Neurons with Temporal Coding
inferred← citeshttps://doi.org/10.1006/jcss.1997.1479openalex-snapshot
Neural Networks with Quadratic VC Dimension
inferred← citeshttps://doi.org/10.1006/jcss.1997.1480openalex-snapshot
On the Computation of Boolean Functions by Analog Circuits of Bounded Fan-In
inferred← citeshttps://doi.org/10.1006/jcss.1997.1507openalex-snapshot
Learning Distributions by Their Density Levels: A Paradigm for Learning without a Teacher
inferred← citeshttps://doi.org/10.1006/jcss.2001.1810openalex-snapshot
Aggregate Operators in Constraint Query Languages
inferred← citeshttps://doi.org/10.1007/978-3-030-25540-4_15openalex-snapshot
Efficient Synthesis with Probabilistic Constraints
inferred← citeshttps://doi.org/10.1007/978-3-319-23392-5_6openalex-snapshot
Genetic Programming Model Regularization
inferred← citeshttps://doi.org/10.1007/bf00115300openalex-snapshot
PAC Learning of One-Dimensional Patterns
inferred← citeshttps://doi.org/10.1007/s10791-026-10123-yopenalex-snapshot
Interpretability-preserving knowledge distillation via multi-granular feature alignment for resource-efficient CNNs
inferred← citeshttps://doi.org/10.1007/s42484-020-00027-5openalex-snapshot
Pseudo-dimension of quantum circuits
inferred← citeshttps://doi.org/10.1016/0020-0190(96)00120-2openalex-snapshot
A size-depth trade-off for the analog computation of Boolean functions
inferred← citeshttps://doi.org/10.1016/j.jda.2011.10.002openalex-snapshot
Indexability, concentration, and VC theory
inferred← citeshttps://doi.org/10.1016/j.jfranklin.2021.08.023openalex-snapshot
On the stability and generalization of neural networks with VC dimension and fuzzy feature encoders
inferred← citeshttps://doi.org/10.1016/j.jmateco.2021.102571openalex-snapshot
Uniformly supported approximate equilibria in families of games
inferred← citeshttps://doi.org/10.1016/j.neunet.2017.07.002openalex-snapshot
Error bounds for approximations with deep ReLU networks
inferred← citeshttps://doi.org/10.1016/j.neunet.2018.08.010openalex-snapshot
The Vapnik–Chervonenkis dimension of graph and recursive neural networks
inferred← citeshttps://doi.org/10.1016/j.neunet.2020.04.001openalex-snapshot
A tight upper bound on the generalization error of feedforward neural networks
inferred← citeshttps://doi.org/10.1016/j.neunet.2022.06.013openalex-snapshot
Approximation in shift-invariant spaces with deep ReLU neural networks
inferred← citeshttps://doi.org/10.1016/j.neunet.2023.06.004openalex-snapshot
Approximation of classifiers by deep perceptron networks
inferred← citeshttps://doi.org/10.1016/j.ymssp.2021.108153openalex-snapshot
Perception modelling by invariant representation of deep learning for automated structural diagnostic in aircraft maintenance: A study case using DeepSHM
inferred← citeshttps://doi.org/10.1016/s0166-218x(98)00000-6openalex-snapshot
Combinatorial variability of vapnik-chervonenkis classes with applications to sample compression schemes
inferred← citeshttps://doi.org/10.1016/s0166-218x(98)00014-6openalex-snapshot
Vapnik-Chervonenkis dimension of recurrent neural networks
inferred← citeshttps://doi.org/10.1016/s0167-6911(98)00006-1openalex-snapshot
A learning result for continuous-time recurrent neural networks
inferred← citeshttps://doi.org/10.1016/s0167-6911(98)00008-5openalex-snapshot
The complexity of model classes, and smoothing noisy data
inferred← citeshttps://doi.org/10.1016/s0304-3975(97)00028-5openalex-snapshot
Dynamical recognizers: real-time language recognition by analog computers
inferred← citeshttps://doi.org/10.1016/s0890-5401(03)00015-4openalex-snapshot
Learning from rounded-off data
inferred← citeshttps://doi.org/10.1016/s0893-6080(97)00011-7openalex-snapshot
Networks of spiking neurons: The third generation of neural network models
inferred← citeshttps://doi.org/10.1016/s0925-2312(99)00113-7openalex-snapshot
Simple sample bound for feedforward sigmoid networks with bounded weights
inferred← citeshttps://doi.org/10.1016/s1474-6670(17)41660-0openalex-snapshot
Remarks on the Sample Complexity for Linear Control Systems Identification
inferred← citeshttps://doi.org/10.1016/s1566-2535(00)00004-xopenalex-snapshot
Finite sample performance guarantees of fusers for function estimators
inferred← citeshttps://doi.org/10.1023/a:1007447530834openalex-snapshot
Localization vs. Identification of Semi-Algebraic Sets
inferred← citeshttps://doi.org/10.1023/a:1007609822199openalex-snapshot
On the Sample Complexity for Nonoverlapping Neural Networks
inferred← citeshttps://doi.org/10.1023/a:1007681724516openalex-snapshot
A Theoretical and Empirical Study of a Noise-Tolerant Algorithm to Learn Geometric Patterns
inferred← citeshttps://doi.org/10.1109/18.532888openalex-snapshot
Sample complexity for learning recurrent perceptron mappings
inferred← citeshttps://doi.org/10.1109/18.661502openalex-snapshot
The sample complexity of pattern classification with neural networks: the size of the weights is more important than the size of the network
inferred← citeshttps://doi.org/10.1109/ccc.1996.507671openalex-snapshot
VC dimension in circuit complexity
inferred← citeshttps://doi.org/10.1109/cdc.1996.573118openalex-snapshot
The complexity of model classes and smoothing noisy data
inferred← citeshttps://doi.org/10.1109/eeei.2000.924435openalex-snapshot
Local polynomial models for classification
inferred← citeshttps://doi.org/10.1109/icmla.2004.1383513openalex-snapshot
Agnostic learning of general geometric patterns and multi-instance learning in R/sup d/
inferred← citeshttps://doi.org/10.1109/ijcnn.2000.860767openalex-snapshot
VC dimension bounds for product unit networks
inferred← citeshttps://doi.org/10.1109/ijcnn.2004.1380064openalex-snapshot
On learning a function of perceptrons
inferred← citeshttps://doi.org/10.1109/isit54713.2023.10206618openalex-snapshot
Upper bounds on the Natarajan dimensions of some function classes
inferred← citeshttps://doi.org/10.1109/sfcs.1995.492470openalex-snapshot
Approximating the volume of definable sets
inferred← citeshttps://doi.org/10.1109/tcyb.2022.3177573openalex-snapshot
Semisupervised Manifold Regularization via a Subnetwork-Based Representation Learning Model
inferred← citeshttps://doi.org/10.1109/tmm.2024.3453044openalex-snapshot
Coarse-to-Fine Target Detection for HFSWR With Spatial-Frequency Analysis and Subnet Structure
inferred← citeshttps://doi.org/10.1109/tnn.2004.832810openalex-snapshot
On the Sample Complexity of Learning for Networks of Spiking Neurons With Nonlinear Synaptic Interactions
inferred← citeshttps://doi.org/10.1109/tnnls.2012.2204773openalex-snapshot
Discretized-Vapnik-Chervonenkis Dimension for Analyzing Complexity of Real Function Classes
inferred← citeshttps://doi.org/10.1109/tnnls.2013.2296046openalex-snapshot
Deep Networks are Effective Encoders of Periodicity
inferred← citeshttps://doi.org/10.1145/1862344.1862346openalex-snapshot
Indexability, concentration, and VC theory
inferred← citeshttps://doi.org/10.1145/1995412.1995419openalex-snapshot
Lower bounds on performance of metric tree indexing schemes for exact similarity search in high dimensions
inferred← citeshttps://doi.org/10.1145/303976.303987openalex-snapshot
Exact and approximate aggregation in constraint query languages
inferred← citeshttps://doi.org/10.1145/3397476openalex-snapshot
Coin Flipping in Dynamic Programming Is Almost Useless
inferred← citeshttps://doi.org/10.1162/08997660252741121openalex-snapshot
On the Complexity of Computing and Learning with Multiplicative Neural Networks
inferred← citeshttps://doi.org/10.1162/089976698300017016openalex-snapshot
Almost Linear VC-Dimension Bounds for Piecewise Polynomial Networks
inferred← citeshttps://doi.org/10.1162/089976698300017359openalex-snapshot
On the Effect of Analog Noise in Discrete-Time Analog Computations
inferred← citeshttps://doi.org/10.1214/21-aos2084openalex-snapshot
Marginal singularity and the benefits of labels in covariate-shift
inferred← citeshttps://doi.org/10.14711/thesis-991013100058803412openalex-snapshot
Learning distributions by generative adversarial networks : approximation and generalization
inferred← citeshttps://doi.org/10.1609/aaai.v30i1.10243openalex-snapshot
On the Depth of Deep Neural Networks: A Theoretical View
inferred← citeshttps://doi.org/10.22331/q-2023-06-07-1036openalex-snapshot
Clifford Circuits can be Properly PAC Learned if and only if RP=NP
inferred← citeshttps://doi.org/10.24963/kr.2020/27openalex-snapshot
Reasoning about Measures of Unmeasurable Sets
Editorial review for independent work. ICSAC — the Institute for Complexity
Science and Advanced Computing — runs a structured editorial panel for researchers
publishing outside journals, and publishes on CiteStamp rails. Submission is free
and goes through ORCID: icsacinstitute.org/submit.