akhvorov/vgram
Feature extraction from sequential data
This tool helps analyze sequences like text documents or event logs by identifying the most meaningful patterns. You provide a collection of sequences, and it learns an 'alphabet' of informative sub-sequences. This compressed representation can then be used for tasks like document classification or anomaly detection, benefiting data scientists and machine learning engineers working with sequential data.
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Use this if you need to extract robust, meaningful features from collections of text, code, or other sequential data to improve machine learning model performance.
Not ideal if your data is unstructured images, audio, or tabular data, or if you need to analyze relationships between different types of data simultaneously.
Stars
7
Forks
—
Language
C++
License
MIT
Category
Last pushed
Jul 04, 2019
Commits (30d)
0
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