The DOI matches its Crossref record; 12 of the 12 references it cites were found in a registry or an index.
10.1162/153244302760200687
Abstract
This DOI is not currently attached to any metadata records. DOIs can’t actually ever be deleted (they’re persistent), but sometimes our members create DOIs in error. We do have a process to approximate deletion which we follow only in rare cases where the DOI has been genuinely created in error, and most crucially, if the DOI has never been published anywhere online or in print and never otherwise distributed to or communicated with anyone (authors, readers, reviewers, etc.
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
The Nature of Statistical Learning Theory
Vapnik, V. N. (1995) · doi:10.1007/978-1-4757-2440-0 · Cited by 39,138
An Introduction to Support Vector Machines and Other Kernel-based Learning Methods
Cristianini, N., & Shawe-Taylor, J. (2000) · doi:10.1017/cbo9780511801389 · Cited by 13,855
A training algorithm for optimal margin classifiers
Boser, B. E., Guyon, I. M., & Vapnik, V. N. (1992) · Proceedings of the fifth annual workshop on Computational learning theory · doi:10.1145/130385.130401 · Cited by 11,992
Text categorization with Support Vector Machines: Learning with many relevant features
Joachims, T. (1998) · Lecture Notes in Computer Science · doi:10.1007/bfb0026683 · Cited by 7,999
A vector space model for automatic indexing
Salton, G., Wong, A., & Yang, C. S. (1975) · Communications of the ACM · doi:10.1145/361219.361220 · Cited by 7,521
Making Large-Scale SVM Learning Practical
Joachims, T. (1998) · RePEc: Research Papers in Economics · doi:10.17877/de290r-14262 · Cited by 4,411
XVI. Functions of positive and negative type, and their connection the theory of integral equations
Mercer, J. (1909) · Philosophical Transactions of the Royal Society of London. Series A, Containing Papers of a Mathematical or Physical Character · doi:10.1098/rsta.1909.0016 · Cited by 2,001
Using the Nyström Method to Speed Up Kernel Machines
Williams, C. K. I., & Seeger, M. (2000) · Cited by 1,646
Input space versus feature space in kernel-based methods
Scholkopf, B., Mika, S., Burges, C. J. C., Knirsch, P., Muller, K.-R., Ratsch, G., & Smola, A. J. (1999) · IEEE Transactions on Neural Networks · doi:10.1109/72.788641 · Cited by 1,164
Convolution kernels on discrete structures
Haussler, D. H. (1999) · Cited by 1,089
Cited by
A brief survey on sequence classification
Xing, Z., Pei, J., & Keogh, E. (2010) · ACM SIGKDD Explorations Newsletter · doi:10.1145/1882471.1882478 · Cited by 553
Discriminative Frequent Pattern Analysis for Effective Classification
Cheng, H., Yan, X., Han, J., & Hsu, C.-W. (2007) · 2007 IEEE 23rd International Conference on Data Engineering · doi:10.1109/icde.2007.367917 · Cited by 357
Effective Pattern Discovery for Text Mining
Zhong, N., Li, Y., & Wu, S.-T. (2012) · IEEE Transactions on Knowledge and Data Engineering · doi:10.1109/tkde.2010.211 · Cited by 340
LogSig: generating system events from raw textual logs
Tang, L., Li, T., & Perng, C.-S. (2011) · Proceedings of the 20th ACM international conference on Information and knowledge management · doi:10.1145/2063576.2063690 · Cited by 244
DeepMood: Modeling Mobile Phone Typing Dynamics for Mood Detection
Cao, B., Zheng, L., Zhang, C., Yu, P. S., Piscitello, A., Zulueta, J., Ajilore, O., Ryan, K., & Leow, A. D. (2017) · Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining · doi:10.1145/3097983.3098086 · Cited by 145
Metadata
- Venue
- CrossRef Listing of Deleted DOIs
- Publisher
- Unmaintained records
- Published
- 2000
- Volume
- 1
- ISSN
- 0003-6951 (ISSN-L)
- OpenAlex
- W1480643256
- Cited by
- 526
- References
- 12
- 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: 10.1162/153244302760200687. https://app.cochat.ai/papers/10.1162/153244302760200687
- The paper
- 10.1162/153244302760200687. (2000). CrossRef Listing of Deleted DOIs. https://doi.org/10.1162/153244302760200687
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.
