Machine learningMachine learning

Polu-nadgledano online učenje

Polu-nadgledano online učenje kombinira stil inkrementalnog ažuriranja online učenja sa sposobnošću iskorištavanja neoznačenih primjera, omogućujući modelima kontinuirano poboljšanje iz toka podataka u kojem samo mali dio pristiglih instanci nosi oznake stvarnih vrijednosti. Posebno je vrijedno kada je označavanje skupo ili odgođeno, ali podaci pristižu u stvarnom vremenu.

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Method map

The neighbourhood of related methods — select a node to explore.

Izvori

  1. Goldberg, A., Li, M., & Zhu, X. (2008). Online manifold regularization: A new learning setting and empirical study. In Proceedings of the European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases (ECML PKDD 2008), Lecture Notes in Computer Science, 5211, 393–407. Springer. link
  2. Zhu, X., & Goldberg, A. B. (2009). Introduction to Semi-Supervised Learning. Morgan & Claypool Publishers. ISBN: 978-1-59829-548-3

Kako citirati ovu stranicu

ScholarGate. (2026, June 3). Semi-supervised Online Learning (Incremental Learning with Partially Labeled Streams). ScholarGate. https://scholargate.app/hr/machine-learning/semi-supervised-online-learning

Which method?

Set this method beside its closest kin and read them side by side — the library lays the books on the table; the choice is yours.

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Citirana u

ScholarGateSemi-supervised Online Learning (Semi-supervised Online Learning (Incremental Learning with Partially Labeled Streams)). Preuzeto 2026-06-15 s https://scholargate.app/hr/machine-learning/semi-supervised-online-learning · Skup podataka: https://doi.org/10.5281/zenodo.20539026