Name
Abstract | ||
|---|---|---|
This paper examines a matrix-regularized multiple kernel learning (MKL) technique based on a notion of (r, p) norms. For the problem of learning a linear combination in the support vector machine-based framework, model complexity is typically controlled using various regularization strategies on the combined kernel weights. Recent research has developed a generalized ℓp-norm MKL framework with tun... |
Year | DOI | Venue |
|---|---|---|
2018 | 10.1109/TNNLS.2017.2785329 | IEEE Transactions on Neural Networks and Learning Systems |
Keywords | Field | DocType |
Kernel,Support vector machines,Complexity theory,Learning systems,Convergence,Optimization,Algorithm design and analysis | Kernel (linear algebra),Convergence (routing),Discrete mathematics,Linear combination,Pattern recognition,Computer science,Matrix (mathematics),Multiple kernel learning,Support vector machine,Regularization (mathematics),Artificial intelligence,Model complexity | Journal |
Volume | Issue | ISSN |
29 | 10 | 2162-237X |
Citations | PageRank | References |
4 | 0.38 | 13 |
Authors | ||
5 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Yina Han | 1 | 45 | 4.74 |
| Yixin Yang | 2 | 33 | 11.80 |
| Xuelong Li | 3 | 15049 | 617.31 |
| Qingyu Liu | 4 | 4 | 0.38 |
| Yuanliang Ma | 5 | 5 | 0.75 |