arXiv (All)
open access
Stochastic tensor space feature theory with applications to robust machine learning
In this paper we develop a Multilevel Orthogonal Subspace (MOS) Karhunen--Loève feature theory based on stochastic tensor spaces, for the construction of robust machine learning features. Training data are treated as instances of a random field within a relevant Bochner space. Our key observation is
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
- Full-Covariance Smoothing of Bayesian Neural Networks for Online Adaptation#1063455
- Stochastic Trace and Diagonal Estimator for Tensors#448017
- Robust LassoNet: Enhancing Feature Selection in Neural Networks via Robust Loss Functions#1142864
- Dynamical mean-field theory for stochastic gradient descent in Gaussian mixture classification#27547
- LHW-Net: An ensemble-based machine learning framework for brain tumor classification.#122721
Record · ID 1013358
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