arXiv (All)
open access
Robust Error Bounds for Vector-Valued Kernel Ridge Regression
While kernel-based learning methods for scalar-valued functions have been extensively studied, comparatively little work considers nonlinear vector-valued operators. Such operators with vector-valued outputs, e.g., state vectors and trajectories, commonly arise in system identification problems. In
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
- Sharp Sobolev Sandwich and Approximation Rates of Radon-Domain $L^p$ Ridge Integral Spaces for ReLU$^k$ Networks#1122474
- Structural interpretability in SVMs with truncated orthogonal polynomial kernels#18996
- Goal-Oriented Lower-Tail Calibration of Gaussian Processes for Bayesian Optimization#204771
- Perceived Risks and Benefits of Using a Survival and Functional Outcome Machine Learning Model for Glioblastoma#939412
- Analyse et développement de nouveaux optimiseurs en Machine Learning. (Analysis and development of new optimizers in Machine Learning).#489526
Record · ID 1014037
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