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

{{short description|Machine learning algorithm}} In [[machine learning]] (ML), a '''margin classifier''' is a type of [[Statistical classification ...

...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 ...

...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, ...

...g data applications, including [[recommendation algorithm]]s and [[machine learning]].<ref name=Abuzaid>{{cite arXiv |last1=Abuzaid |first1=Firas |last2=Sethi ...is generally too slow to be used on practical problems. However, efficient algorithms exist to speed up MIPS search.<ref name="Abuzaid" /><ref>Steve Mussmann, St ...

...r | first1 = Michael | last2 = Vassilvitskii | first2 = Sergei | chapter = Algorithms with Predictions | date = 31 December 2020 | pages = 646–662 | publisher = ...gular algorithms just the problem instance is inputted, learning augmented algorithms accept an extra parameter. ...

{{Short description|Machine learning algorithm}} | title = Ultraconservative Online Algorithms for Multiclass Problems ...

.... 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. ...

{{Short description|Machine learning method}} {{Machine learning bar}} ...

{{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 ...

{{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 ...

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

...019}}{{Short description|Description of limiting behavior in probabilistic algorithms}} ...thm for verifying matrix multiplication. It runs faster than deterministic algorithms WHP. ...

...irosi1990">T. Poggio and F. Girosi (1990). "Networks for Approximation and Learning". ''Proc. IEEE'' '''Vol. 78, No. 9''':1481-1497.</ref><ref name="Mahdi">R.N ...ref name="Schwenker">F. Schwenker, H.A. Kestler and G. Palm (2001). "Three Learning Phases for Radial-Basis-Function Network" ''Neural Netw.'' '''14''':439-458 ...

{{Short description|Open-source machine learning system for end-to-end data science lifecycle}} ...)|Java]], [[Python (programming language)|Python]], [[(Descriptive Machine Learning)| DML]], [[C (programming language)|C]] ...

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

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

{{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 ...

...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== ...

{{Short description|A classification model in machine learning based on centroids}} In [[machine learning]], a '''nearest centroid classifier''' or '''nearest prototype classifier'' ...