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{{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 ...

{{Machine learning bar}} ...g''' (sometimes abbreviated '''action learning''') is an area of [[machine learning]] concerned with creation and modification of [[software agent]]'s knowledg ...

{{Short description|Reinforcement learning technique}} {{Machine learning|Reinforcement learning}} ...

In the field of [[statistical learning theory]], '''matrix regularization''' generalizes notions of vector regular ...nd these can be generalized to the nonparametric case of [[multiple kernel learning]]. ...

{{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 ...

...einforcement learning]] problems, using multiscale spectral and [[manifold learning]] methods. Proto-value functions are generated by [[Spectral graph theory| ...orcement Learning]. Proceedings of the International Conference on Machine Learning [[ICML]] 2005</ref> ...

...ollaborative Filtering, and Data Visualization |journal=Journal of Machine Learning Research |date=October 2000 |url=http://www.jmlr.org/papers/volume1/heckerm ...hard.<ref>{{cite journal |last1=HECKERMAN |first1=David|title=Large-Sample Learning of Bayesian Networks is NP-Hard|year=2012|arxiv=1212.2468|url=http://www.jm ...

{{Short description|Reinforcement learning method}} ...f error-driven learning in simple two-layer networks from a discriminative learning perspective |url=https://doi.org/10.3758/s13428-021-01711-5 |journal=Behavi ...

...[[machine learning]], and provides large sample justifications for certain learning algorithms. {{See also|Statistical learning theory}} ...

...for performing semi-supervised learning in cases where [[Feature (machine learning)|data features]] may be redundant.<ref name="Collins99" /> ...id boosting algorithm in the [[Probably approximately correct learning|PAC learning]] sense. ...

{{short description|Field associated with machine learning and transfer learning}} ...ng|thumb|Distinction between usual machine learning setting and [[transfer learning]], and positioning of domain adaptation]] ...

{{Short description|Machine learning paradigm}} {{distinguish|Semi-supervised learning}} ...

{{Short description|Problem in machine learning and statistical classification}} {{machine learning}} ...

{{Short description|Machine learning practice of supervised learning}} ...mation'', or ''class prior estimation'') is the task of using [[supervised learning]] in order to train models (''quantifiers'') that estimate the [[empirical ...

{{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 ...

...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]] ...

{{About|a technique used in machine learning||Highway}} ...th hundreds of layers, much deeper than previous [[Neural network (machine learning)|neural networks]].<ref name="highway2015" /><ref name="highway2015neurips" ...

...-output learning or vector-valued learning), [[Inductive transfer|transfer learning]], and co-[[kriging]]. [[Multi-label classification]] can be interpreted as ...in learning vector-valued functions was particularly sparked by multitask learning, a framework which tries to learn multiple, possibly different tasks simult ...

.... It was an instrumental step in the evolution towards [[Transformer (deep learning architecture)|transformer-based]] language modelling. ...ithin the sentence. [[GloVe]] and [[Word2vec|Word2Vec]] built upon this by learning fixed vector representations (embeddings) for words based on their co-occur ...

...'''basis discovery''', is a technique that finds a set of general-purpose (task-independent) [[basis functions]] to simplify complex data ([[State space (c In [[reinforcement learning]] (RL), many real-world problems modeled as [[Markov Decision Processes]] ( ...