Learning to rank on graphs
From MaRDI portal
Publication:1959630
DOI10.1007/s10994-010-5185-8zbMath1470.68068OpenAlexW2100364402MaRDI QIDQ1959630
Publication date: 7 October 2010
Published in: Machine Learning (Search for Journal in Brave)
Full work available at URL: https://doi.org/10.1007/s10994-010-5185-8
Learning and adaptive systems in artificial intelligence (68T05) Graph theory (including graph drawing) in computer science (68R10) Hilbert spaces with reproducing kernels (= (proper) functional Hilbert spaces, including de Branges-Rovnyak and other structured spaces) (46E22) Computational methods for problems pertaining to biology (92-08)
Related Items
A review on instance ranking problems in statistical learning, Properties of vector embeddings in social networks, Fuzzy collaborative clustering-based ranking approach for complex objects, Quantitative robustness of instance ranking problems, The performance of semi-supervised Laplacian regularized regression with the least square loss, Query-dependent ranking and its asymptotic properties, Generalization performance of bipartite ranking algorithms with convex losses, Stability analysis of learning algorithms for ontology similarity computation, Unnamed Item, Analysis of k-partite ranking algorithm in area under the receiver operating characteristic curve criterion
Uses Software
Cites Work
- Unnamed Item
- Unnamed Item
- Unnamed Item
- Unnamed Item
- Unnamed Item
- Unnamed Item
- Unnamed Item
- Support Vector Ordinal Regression
- Semi-supervised learning on Riemannian manifolds
- Some properties of invariant sets of a flow
- Laplacians and the Cheeger inequality for directed graphs
- Small ball probabilities for a shifted Poisson process
- Ranking and empirical minimization of \(U\)-statistics
- Authoritative sources in a hyperlinked environment
- Graph-Based Semi-Supervised Learning and Spectral Kernel Design
- Statistical Analysis of Bayes Optimal Subset Ranking
- Learning Theory
- Laplacian Eigenmaps for Dimensionality Reduction and Data Representation
- 10.1162/1532443041827916
- Learning Theory and Kernel Machines
- An Alternative Ranking Problem for Search Engines
- Learning Theory
- Online Ranking by Projecting
