The DOI matches its Crossref record; 1 of the 1 references it cites were found in a registry or an index.
IEEE Transactions on Pattern Analysis and Machine Intelligence
Abstract
A primary computational problem in kernel regression is solution of a dense linear system with the N × N kernel matrix.Because a direct solution has an O(N 3 ) cost, iterative Krylov methods are often used with fast matrix-vector products.For poorly conditioned problems, convergence of the iteration is slow and preconditioning becomes necessary.We investigate preconditioning from the viewpoint of scalability and efficiency.The problems that conventional preconditioners face when applied to kernel methods are demonstrated.A novel flexible preconditioner that not only improves convergence but also allows utilization of fast kernel matrixvector products is introduced.The performance of this preconditioner is first illustrated on synthetic data, and subsequently on a suite of test problems in kernel regression and geostatistical kriging.
Overview
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Citations
Cites
Numerical Methods for Large Eigenvalue Problems: Revised Edition
Saad, Y. (2011) · doi:10.1137/1.9781611970739 · Cited by 2,364
Cited by
A programmable diffractive deep neural network based on a digital-coding metasurface array
Liu, C., Ma, Q., Luo, Z. J., Hong, Q. R., Xiao, Q., Zhang, H. C., Miao, L., Yu, W. M., Cheng, Q., Li, L., et al. (2022) · Nature Electronics · doi:10.1038/s41928-022-00719-9 · Cited by 633
A review of object detection based on deep learning
Xiao, Y., Tian, Z., Yu, J., Zhang, Y., Liu, S., Du, S., & Lan, X. (2020) · Multimedia Tools and Applications · doi:10.1007/s11042-020-08976-6 · Cited by 584
Morphological diversity of single neurons in molecularly defined cell types
Peng, H., Xie, P., Liu, L., Kuang, X., Wang, Y., Qu, L., Gong, H., Jiang, S., Li, A., Ruan, Z., et al. (2021) · Nature · doi:10.1038/s41586-021-03941-1 · Cited by 417
A Genome-Wide Association Study Identifies Five Loci Influencing Facial Morphology in Europeans
Liu, F., van der Lijn, F., Schurmann, C., Zhu, G., Chakravarty, M. M., Hysi, P. G., Wollstein, A., Lao, O., de Bruijne, M., Ikram, M. A., et al. (2012) · PLoS Genetics · doi:10.1371/journal.pgen.1002932 · Cited by 326
Weakly-supervised learning for lung carcinoma classification using deep learning
Kanavati, F., Toyokawa, G., Momosaki, S., Rambeau, M., Kozuma, Y., Shoji, F., Yamazaki, K., Takeo, S., Iizuka, O., & Tsuneki, M. (2020) · Scientific Reports · doi:10.1038/s41598-020-66333-x · Cited by 250
Metadata
- Venue
- IEEE Transactions on Pattern Analysis and Machine Intelligence
- Published
- 2004-03
- Volume
- 26
- Issue
- 3
- Pages
- 02-02
- ISSN
- 0162-8828 (ISSN-L)
- 2160-9292
- OpenAlex
- W1676314349
- Cited by
- 3,721
- References
- 1
- FWCI
- 31.97
- Type
- journal article
- Language
- en
- Open access
- closed
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
How to cite
Cite CoChat for the checks on this page, and the paper for its own findings.
- This page
- CoChat. (2026). Citation check: IEEE Transactions on Pattern Analysis and Machine Intelligence. https://app.cochat.ai/papers/10.1109/tpami.2004.1307293
- The paper
- IEEE Transactions on Pattern Analysis and Machine Intelligence. (2004). IEEE Transactions on Pattern Analysis and Machine Intelligence. https://doi.org/10.1109/tpami.2004.1307293
Data sources
Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
