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| Content Provider | Springer Nature Link |
|---|---|
| Author | Apolloni, B. Malchiodi, D. Taylor, J. G. |
| Copyright Year | 2013 |
| Abstract | We cope with the key step of bootstrap methods of generating a possibly infinite sequence of random data preserving properties of the distribution law, starting from a primary sample actually drawn from this distribution. We solve this task in a cooperative way within a community of generators where each improves its performance from the analysis of the other partners’ production. Since the analysis is based on an a priori distrust of the other partners’ production, we denote the partner ensemble as a gossip community and denote the statistical procedure learning by gossip. We prove that this procedure is highly efficient when applied to the elementary problem of reproducing a Bernoulli distribution, with a properly moderated distrust rate when the absence of a long-term memory requires an online estimation of the bootstrap generator parameters. This fact makes the procedure viable as a basic template of an efficient interaction scheme within social network agents. |
| Starting Page | 327 |
| Ending Page | 339 |
| Page Count | 13 |
| File Format | |
| ISSN | 18669956 |
| Journal | Cognitive Computation |
| Volume Number | 5 |
| Issue Number | 3 |
| e-ISSN | 18669964 |
| Language | English |
| Publisher | Springer US |
| Publisher Date | 2013-03-09 |
| Publisher Place | Boston |
| Access Restriction | One Nation One Subscription (ONOS) |
| Subject Keyword | Online bootstrap Neural networks Learning algorithms Social networks Neurosciences Computation by Abstract Devices Artificial Intelligence (incl. Robotics) Computational Biology/Bioinformatics |
| Content Type | Text |
| Resource Type | Article |
| Subject | Cognitive Neuroscience Computer Science Applications Computer Vision and Pattern Recognition |
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