Dataset pruning is an approach to reduce a large dataset to obtain a small dataset by removing less significant sample.

The table uniformity approach is proposed to solve the problem of dialect determination. The method is based on consistency measurement over a table Γδ
, which has been returned by parsing a CSV file with a dialect ρδ
, and the dispersion of records along with the inference of raw data types from fields.

TbUM

Detecting CSV File Dialects by Table Uniformity Measurement and Data Type Inference

Dataset Pruning

Dataset Pruning: Reducing Training Data by Examining Generalization Influence

The discrete analogue of the Gabor transform is obtained by choosing the temporal window function in a time-frequency analysis as the discrete analogue of the Gaussian kernel.

Source	Dataset Pruning: Reducing Training Data by Examining Generalization Influence
Year
Data Source	Papers with Code archive (CC BY-SA)