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In [[machine learning]] and [[computational learning theory]], '''LogitBoost''' is a [[Boosting (meta-algorithm)|boosting]] algorithm f ...ginners |date=22 September 2023 |url=https://www.prodigitalweb.com/machine-learning-algorithms-for-beginners/ |access-date=2023-10-01 |language=en-US}}</ref> ...

...g|title=A Generalization of Sauer's Lemma|journal=Journal of Combinatorial Theory|volume=71|pages=219–240|year=1995}}</ref> ...sity Press|year=2013}}</ref> present comprehensive material on multi-class learning and the Natarajan dimension, including uniform convergence and learnability ...

In [[computational complexity theory]], a '''log-space computable function''' is a function <math>f\colon \Sigma ...ks?id=1aMKAAAAQBAJ Introduction to the Theory of Computation]'', [[Cengage Learning]], {{ISBN|978-0-619-21764-8}}. ...

{{Short description|Model of algorithmic learning}} {{Machine learning|Theory}} ...

...it is not possible to access noise-free data. Noise can interfere with the learning process at different levels: the algorithm may receive data that have been ==Notation and the Valiant learning model== ...

In the theory of parallel algorithms, the '''1-vs-2 cycles problem''' concerns a simplifi ...''1-vs-2 cycles conjecture''' or '''2-cycle conjecture''' is an unproven [[computational hardness assumption]] asserting that solving the 1-vs-2 cycles problem in t ...

In [[computational complexity theory]], a '''log space transducer (LST)''' is a type of [[Turing machine]] used ...ks?id=1aMKAAAAQBAJ Introduction to the Theory of Computation]'', [[Cengage Learning]], {{ISBN|978-0-619-21764-8}}. ...

{{Short description|Statement in computational learning theory}} ...ical motivations for the use of non-linear [[kernel methods]] in [[machine learning]] applications. It is so termed after the information theorist [[Thomas M. ...

...pace]]. Diffusion wavelets are an extension of classical [[Wavelet|wavelet theory]] from [[harmonic analysis]]. Unlike classical wavelets whose basis functio ...ald |author2=Mauro Maggioni |title=Diffusion Wavelets |journal=Applied and Computational Harmonic Analysis |date=May 2008 |volume=24 |issue=3 |pages=329–353 |url=ht ...

{{Short description|Supervised machine learning techniques}} {{Machine learning|Problems}} ...

...nid|title=Classical complexity and quantum entanglement|journal=Journal of Computational Science|date=2004|volume=69|issue=3 |pages=448–484|doi=10.1016/j.jcss.2004. ...al Natural Language Learning}}</ref> This improves the training of machine learning algorithms, in situations where [[maximum likelihood]] training may not be ...

...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 ...

...f>{{Cite book |url=https://www.worldcat.org/oclc/1005114370 |title=Machine Learning and Knowledge Extraction : First IFIP TC 5, WG 8.4, 8.9, 12.9 International ...brunner |first=Herbert |url=https://www.worldcat.org/oclc/946298151 |title=Computational topology : an introduction |date=2010 |publisher=American Mathematical Soci ...

...| title=Solving high-dimensional partial differential equations using deep learning | journal=Proceedings of the National Academy of Sciences | volume=115 | is | series = Probability theory and stochastic modeling ...

{{Short description|Notion in computational learning theory}} ...9 examples of handwritten letters and their labels are available. A stable learning algorithm would produce a similar [[statistical classification|classifier]] ...

...tor machine]]s (SVMs) in the context of other regularization-based machine-learning algorithms. SVM algorithms categorize binary data, with the goal of fittin ...rything Old is New Again: A Fresh Look at Historical Approaches in Machine Learning |year=2002 |publisher=MIT (PhD thesis) |url=http://web.mit.edu/~9.520/www/P ...

...].<ref name="Val84">[http://dl.acm.org/citation.cfm?id=1972 L. Valiant ''A theory of the learnable''. Communications of ACM, 1984]</ref> ...mework has been used in a large variety of different fields like [[machine learning]], [[approximation algorithms]], [[applied probability]] and [[statistics]] ...

...sor at the [[University of California, Berkeley]], known for his work on [[computational neuroscience]], [[vision science]], and [[Neural coding|sparse coding]]. He ...hology, [[Cornell University]] and Center for Biological and Computational Learning, [[Massachusetts Institute of Technology]].<ref>{{Cite web |title=Bruno's c ...

{{Short description|Attribute of machine learning models}} {{Machine learning bar}} ...

...d=36137032 |arxiv=2106.12627|s2cid=235624289 }}</ref> For example, machine learning models could learn to solve [[ground state]]s of quantum many-body systems ## Perform a computational basis measurement on <math>\rho_{i}</math> for an outcome <math>b_{i} \in \ ...