On the public citation graph

Bivariate Probability Density Function Discrimination and Artificial Data Generation for Small Datasets Using a Fuzzy Approach

0 signed · 20 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.
Claim with ORCID
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 See whether every reference in this work resolves to a known work and none has been retracted — and get a dated CiteStamped badge you can embed.
See reference health

What this work says

Outgoing claims: the papers this work cites, builds on, or argues with.

inferredcites →https://doi.org/10.1016/j.apenergy.2017.04.007openalex-extracted
A Monte Carlo and PSO based virtual sample generation method for enhancing the energy prediction and energy optimization on small data problem: An empirical study of petrochemical industries
inferredcites →https://doi.org/10.1016/j.asoc.2014.06.056openalex-extracted
An assessment on producing synthetic samples by fuzzy C-means for limited number of data in prediction models
inferredcites →https://doi.org/10.1016/j.cor.2004.11.022openalex-extracted
Using mega-fuzzification and data trend estimation in small data set learning for early FMS scheduling knowledge
inferredcites →https://doi.org/10.1016/j.cor.2005.05.019openalex-extracted
Using mega-trend-diffusion and artificial samples in small data set learning for early flexible manufacturing system scheduling knowledge
inferredcites →https://doi.org/10.1016/j.csda.2011.06.006openalex-extracted
An empirical evaluation of easily implemented, nonparametric methods for generating synthetic datasets
inferredcites →https://doi.org/10.1016/j.dss.2014.06.004openalex-extracted
Generating information for small data sets with a multi-modal distribution
inferredcites →https://doi.org/10.1016/j.dss.2017.10.013openalex-extracted
Rebuilding sample distributions for small dataset learning
inferredcites →https://doi.org/10.1016/j.engappai.2016.12.024openalex-extracted
A PSO based virtual sample generation method for small sample sets: Applications to regression datasets
inferredcites →https://doi.org/10.1016/j.ijar.2003.06.001openalex-extracted
A diffusion-neural-network for learning from small samples
inferredcites →https://doi.org/10.1016/s0165-0114(96)00257-6openalex-extracted
Principle of information diffusion
inferredcites →https://doi.org/10.1080/00207543.2011.555430openalex-extracted
Employing box-and-whisker plots for learning more knowledge in TFT-LCD pilot runs
inferredcites →https://doi.org/10.1080/00207543.2011.631603openalex-extracted
Employing dependent virtual samples to obtain more manufacturing information in pilot runs
inferredcites →https://doi.org/10.1080/00207543.2012.746795openalex-extracted
Employing virtual samples to build early high-dimensional manufacturing models
inferredcites →https://doi.org/10.1080/00207543.2015.1032437openalex-extracted
Practical information diffusion techniques to accelerate new product pilot runs
inferredcites →https://doi.org/10.1080/00207543.2016.1213447openalex-extracted
The attribute-trend-similarity method to improve learning performance for small datasets
inferredcites →https://doi.org/10.1088/1742-6596/892/1/012016openalex-extracted
Handling a Small Dataset Problem in Prediction Model by employ Artificial Data Generation Approach: A Review
inferredcites →https://doi.org/10.1109/72.788640openalex-extracted
An overview of statistical learning theory
inferredcites →https://doi.org/10.1109/iecon.2002.1187512openalex-extracted
Evaluation of membership functions for fuzzy logic controlled induction motor drive
inferredcites →https://doi.org/10.18653/v1/2020.emnlp-main.600openalex-extracted
Textual Data Augmentation for Efficient Active Learning on Tiny Datasets
inferredcites →https://doi.org/10.3390/s23020594openalex-extracted
A Synthetic Data Generation Technique for Enhancement of Prediction Accuracy of Electric Vehicles Demand
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.