Aperta
metadata only
CC BY 4.0
Simultaneous Feature Selection and ANN Training for Data Mining Applications
Feature selection is the method to find which features to be used for successful predictions. In a given dataset to be predicted there can be some inputs that are not important and do not affect the result much. These inputs can be eliminated for simpl
Conceptio Open Archive
Analyze & explore related research in Conceptio
Connect this record directly into your research workflow or AI agent. Explore citations across open repositories, query full text, and export structured proof bundles.
This document is indexed with metadata only: full text is not available in the archive for this record. Open the official source ↗
Related documents
- Stratification Criteria for Machine Learning Pattern Discovery in Particle Physics: Preparing for the AlphaFold Moment#973626
- International Conference on Machine Learning#18331
- Emotion expression in song and speech: Determining acoustic cues through supervised machine learning techniques#295808
- Scikit-learn: Machine Learning in Python#6264
- Improving Wind and Solar Power Prediction with Efficient Wrapper-based Feature Selection: An Empirical Study#370318
Record · ID 1149045
Retrieved via Conceptio: every document is proof-bundled with source, license, and retrieval metadata.
Retrieved via Conceptio: every document is proof-bundled with source, license, and retrieval metadata.
Bring this data into your agent: Connect via MCP or API · 20 free credits to start · no card required
Read the Docs → · View CLI source on GitHub · Conceptio Open Knowledge Archive
Read the Docs → · View CLI source on GitHub · Conceptio Open Knowledge Archive