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- ...r learning''' is an area of research in [[optimization]] and [[statistical learning theory]] which studies algorithms for a general class of [[Convex function# ...atabases |chapter=Solving Structured Sparsity Regularization with Proximal Methods |year=2010|volume=6322|pages=418–433 |doi=10.1007/978-3-642-15883-4_27|seri ...20 KB (2,995 words) - 20:03, 13 May 2024
Page text matches
- ...es, the algorithm is classified to the group of the second order learning methods. It follows a quadratic approximation of the previous [[gradient]] step and ...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
- {{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|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
- ...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
- {{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 algorithm}} | journal = [[Journal of Machine Learning Research]] ...4 KB (530 words) - 15:29, 3 July 2024
- {{Short description|Supervised machine learning techniques}} {{Machine learning|Problems}} ...6 KB (897 words) - 21:14, 1 February 2025
- {{short description|Machine learning algorithm}} In [[machine learning]] (ML), a '''margin classifier''' is a type of [[Statistical classification ...4 KB (629 words) - 22:28, 3 November 2024
- '''Proximal gradient methods''' are a generalized form of projection used to solve non-differentiable [[ ...cent method]] and the [[conjugate gradient method]], but proximal gradient methods can be used instead. ...5 KB (713 words) - 18:45, 26 December 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
- {{machine learning bar}} ...classifiers with online and active learning |journal=[[Journal of Machine Learning Research|JMLR]] |volume=6 |year=2005 |pages=1579–1619}}</ref> ...9 KB (1,362 words) - 23:03, 5 May 2021
- ...input sensory images, usually some extracting features of images. Through learning, some hypothesis of the next action are given and according to the probabil ...n be detected by [[feature detection (computer vision)|feature detection]] methods. ...12 KB (1,908 words) - 14:48, 20 April 2024
- ...ong Lottery Ticket Hypothesis (SLTH)''' is a theoretical framework in deep learning suggesting that sufficiently large random neural networks can contain spars ...s: Finding Sparse, Trainable Neural Networks." International Conference on Learning Representations (ICLR).</ref>, demonstrated that iterative pruning and weig ...6 KB (753 words) - 11:32, 28 February 2025
- {{Short description|Standard testing domain in Reinforced learning}} ...ill. The domain has been used as a [[test bed]] in various [[reinforcement learning]] papers. ...9 KB (1,230 words) - 13:36, 11 November 2024
- {{Short description|Machine learning kernel function}} {{about|machine learning|polynomial kernels in complexity theory|Kernelization}} ...7 KB (1,103 words) - 21:07, 7 September 2024
- {{Short description|Machine learning kernel function}} ...= http://jmlr.org/papers/v11/chang10a.html | journal = Journal of Machine Learning Research | volume = 11 | pages = 1471–1490 }}</ref> ...7 KB (1,002 words) - 00:02, 29 September 2024
- {{Short description|Reinforcement learning algorithms that combine policy and value estimation}} ...Bharath |first4=Anil Anthony |date=November 2017 |title=Deep Reinforcement Learning: A Brief Survey |url=https://ieeexplore.ieee.org/document/8103164 |journal= ...11 KB (1,760 words) - 17:28, 27 January 2025
- ...agents with their own [[memory]] and their [[social learning theory|social learning]] with the knowledge points in the social sharing library. It has been used ...f <math>N_L</math> knowledge points. The algorithm runs in ''T'' iterative learning cycles. By running as a [[Markov chain]] process, the system behavior in th ...6 KB (967 words) - 00:32, 10 October 2021
- In [[machine learning]] and [[data mining]], a '''string kernel''' is a [[Positive-definite kerne Using string kernels with [[Kernel trick|kernelized]] learning algorithms such as [[support vector machine]]s allow such algorithms to wor ...7 KB (984 words) - 16:58, 22 August 2023