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- {{short description|Plot of machine learning model performance over time or experience}} [[File:Learning Curves (Naive Bayes).png|thumb|Learning curve plot of training set size vs training score (loss) and cross-validati ...6 KB (831 words) - 16:35, 27 October 2024
- {{Short description|Machine learning strategy}} ...machine learning method|active learning in the context of education|active learning}} ...18 KB (2,520 words) - 17:49, 7 December 2024
- {{Short description|Educational learning method using computer algorithms and AI}} ...ters adapt the presentation of educational material according to students' learning needs, as indicated by their responses to questions, tasks and experiences. ...17 KB (2,456 words) - 07:32, 29 October 2024
- {{Short description|Machine learning paradigm}} {{distinguish|Semi-supervised learning}} ...18 KB (2,414 words) - 06:48, 17 January 2025
- {{Short description|Social learning process}} '''DeGroot learning''' refers to a rule-of-thumb type of social learning process. The idea was stated in its general form by the American statistici ...11 KB (1,615 words) - 05:08, 6 September 2023
- {{Short description|Supervised learning of a similarity function}} ...regression (machine learning)|regression]] and [[classification in machine learning|classification]], but the goal is to learn a [[similarity function]] that m ...11 KB (1,657 words) - 07:07, 21 December 2024
- {{Machine learning bar}} ...chine learning model "learns". In the [[adaptive control]] literature, the learning rate is commonly referred to as '''gain'''.<ref>{{cite journal |first=Berna ...9 KB (1,303 words) - 11:15, 30 April 2024
- {{short description|Set of learning techniques in machine learning}} {{Machine learning|Problems}} ...45 KB (6,270 words) - 16:45, 5 February 2025
- {{Short description|Branch of machine learning}} {{For|the TV series episode|Deep Learning (South Park)}} ...180 KB (23,460 words) - 20:17, 27 February 2025
- {{short description|Statistics and machine learning technique}} {{Machine learning|Supervised learning}} ...53 KB (7,534 words) - 06:25, 28 February 2025
- {{Short description|Decentralized machine learning}} ...e:Centralized federated learning protocol.png|thumb|Diagram of a Federated Learning protocol with smartphones training a global AI model]] ...51 KB (7,078 words) - 00:30, 16 February 2025
- {{Short description|Machine learning technique where agents learn from demonstrations}} ...e |first4=Chrisina |date=2017-04-06 |title=Imitation Learning: A Survey of Learning Methods |url=https://doi.org/10.1145/3054912 |journal=ACM Comput. Surv. |vo ...12 KB (1,696 words) - 20:17, 6 December 2024
- '''Parity learning''' is a problem in [[machine learning]]. An algorithm that solves this problem must find a function ''ƒ'', given == {{Anchor|LPN}} Noisy version ("Learning Parity with Noise") == ...2 KB (359 words) - 10:37, 9 August 2024
- {{Short description|Subfield of machine learning}} ...hin Ameet 2012}}</ref> Preference learning typically involves [[supervised learning]] using datasets of pairwise preference comparisons, rankings, or other pre ...8 KB (1,185 words) - 05:29, 21 November 2024
- ...me=":0">{{Cite journal |doi = 10.1145/1568318.1568324|title = On lattices, learning with errors, random linear codes, and cryptography|journal = Journal of the ...//portal.acm.org/citation.cfm?id=1536414.1536461.</ref> such as the [[ring learning with errors key exchange]] by Peikert.<ref>{{Cite book|publisher = Springer ...20 KB (3,183 words) - 23:27, 3 May 2024
- {{Short description|Model of algorithmic learning}} {{Machine learning|Theory}} ...11 KB (1,692 words) - 03:07, 25 August 2023
- ...13|doi-access=free }}</ref> A variant of [[Hebbian learning]], competitive learning works by increasing the specialization of each node in the network. It is Models and algorithms based on the principle of competitive learning include [[vector quantization]] and [[self-organizing map]]s (Kohonen maps) ...6 KB (835 words) - 00:05, 17 November 2024
- ...May 2012|chapter=Chapter 2: Learning Processes|date=16 July 1998}}</ref> A learning rule may accept existing conditions (weights and biases) of the network, an ...1995}}</ref> Depending on the complexity of the model being simulated, the learning rule of the network can be as simple as an [[XOR gate]] or [[mean squared e ...9 KB (1,316 words) - 21:00, 27 October 2024
- {{short description|Set of machine learning methods}} {{Machine learning bar}} ...16 KB (2,579 words) - 05:28, 31 July 2024
- ...l.acm.org/citation.cfm?id=195155]</ref> and it was inspired from the [[PAC-learning|PAC-framework]] introduced by [[Leslie Valiant]].<ref name="Val84">[http:// ...mework has been used in a large variety of different fields like [[machine learning]], [[approximation algorithms]], [[applied probability]] and [[statistics]] ...22 KB (3,491 words) - 18:38, 16 April 2022
Page text matches
- '''Parity learning''' is a problem in [[machine learning]]. An algorithm that solves this problem must find a function ''ƒ'', given == {{Anchor|LPN}} Noisy version ("Learning Parity with Noise") == ...2 KB (359 words) - 10:37, 9 August 2024
- ...|authorlink=Ronald L. Rivest|title=Learning decision lists|journal=Machine Learning|volume=2|issue=3|pages=229–246|date=Nov 1987|doi=10.1023/A:1022607331053|ur ...e Efficient Learning of Decision Lists and Parities", ''Journal of Machine Learning Research'' '''7''':12:587-602 [http://dl.acm.org/citation.cfm?id=1248567&dl ...2 KB (325 words) - 17:31, 24 December 2022
- ...Kausik|last=Natarajan|title=On Learning sets and functions|journal=Machine Learning|volume=4|pages=67–97|year=1989|doi=10.1007/BF00114804|doi-access=free}}</re ...sity Press|year=2013}}</ref> present comprehensive material on multi-class learning and the Natarajan dimension, including uniform convergence and learnability ...2 KB (311 words) - 13:26, 19 February 2024
- .... Sometimes, the algorithm is classified to the group of the second order learning methods. It follows a quadratic approximation of the previous [[gradient]] ...ropagation]] algorithm, but the network can behave chaotically during the learning phase due to large step sizes. ...2 KB (272 words) - 16:16, 19 July 2023
- In [[machine learning]] and [[computational learning theory]], '''LogitBoost''' is a [[Boosting (meta-algorithm)|boosting]] algo ...ginners |date=22 September 2023 |url=https://www.prodigitalweb.com/machine-learning-algorithms-for-beginners/ |access-date=2023-10-01 |language=en-US}}</ref> ...2 KB (202 words) - 08:43, 11 December 2024
- {{Short description|Measure of fairness in machine learning models}} ...ment''', is a measure of [[Fairness (machine learning)|fairness in machine learning]]. A classifier satisfies this definition if the subjects in the protected ...2 KB (357 words) - 18:18, 13 May 2024
- ...of prediction error. The inherent difficulty which cost-sensitive machine learning tackles is that minimizing different kinds of classification errors is a [[ Cost-sensitive machine learning optimizes models based on the specific consequences of misclassifications, ...4 KB (528 words) - 22:25, 1 September 2024
- ...ww.researchgate.net/figure/Two-different-sampling-assumptions-for-language-learning-a-Under-the-weak-sampling_fig11_267829053 |website=ResearchGate}}</ref> In ...2 KB (336 words) - 11:40, 8 November 2021
- {{Short description|Concept in probability theory and machine learning}} In the fields of [[machine learning]], the [[theory of computation]], and [[random matrix theory]], a probabili ...2 KB (268 words) - 07:14, 19 September 2024
- {{short description|Plot of machine learning model performance over time or experience}} [[File:Learning Curves (Naive Bayes).png|thumb|Learning curve plot of training set size vs training score (loss) and cross-validati ...6 KB (831 words) - 16:35, 27 October 2024
- {{Short description|Reinforcement learning technique}} {{Machine learning|Reinforcement learning}} ...4 KB (615 words) - 08:52, 11 December 2024
- {{Machine learning bar}} ...g''' (sometimes abbreviated '''action learning''') is an area of [[machine learning]] concerned with creation and modification of [[software agent]]'s knowledg ...7 KB (968 words) - 15:22, 24 February 2025
- {{Short description|Model of algorithmic learning}} {{Machine learning|Theory}} ...11 KB (1,692 words) - 03:07, 25 August 2023
- A '''learning augmented algorithm''' is an [[algorithm]] that can make use of a predictio Whereas in regular algorithms just the problem instance is inputted, learning augmented algorithms accept an extra parameter. ...5 KB (780 words) - 12:03, 17 February 2025
- ...13|doi-access=free }}</ref> A variant of [[Hebbian learning]], competitive learning works by increasing the specialization of each node in the network. It is Models and algorithms based on the principle of competitive learning include [[vector quantization]] and [[self-organizing map]]s (Kohonen maps) ...6 KB (835 words) - 00:05, 17 November 2024
- {{Short description|Machine learning algorithm}} | journal = [[Journal of Machine Learning Research]] ...4 KB (530 words) - 15:29, 3 July 2024
- {{Machine learning bar}} ...chine learning model "learns". In the [[adaptive control]] literature, the learning rate is commonly referred to as '''gain'''.<ref>{{cite journal |first=Berna ...9 KB (1,303 words) - 11:15, 30 April 2024
- {{Short description|Machine Learning concept}} ...w |first3=I. |date=2016 |title=Practical black-box attacks against machine learning |class=cs.CR |eprint=1602.02697}}</ref> ...4 KB (581 words) - 13:30, 21 October 2024
- {{Short description|Machine learning method}} {{Machine learning bar}} ...5 KB (729 words) - 20:29, 30 August 2024
- {{Short description|Machine learning technique}} '''Product of experts''' (PoE) is a [[machine learning]] technique. It models a probability distribution by combining the output f ...3 KB (400 words) - 18:16, 2 December 2024