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...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, ...

...equires only local information of the [[artificial neuron]] to which it is applied. ...ropagation]] algorithm, but the network can behave chaotically during the learning phase due to large step sizes. ...

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

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. ...

{{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 ...

{{Short description|A classification model in machine learning based on centroids}} ...samples whose [[mean]] ([[centroid]]) is closest to the observation. When applied to [[text classification]] using [[vector space model|word vectors]] contai ...

{{Short description|Machine learning calibration technique}} {{machine learning bar}} ...

...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 ...

{{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 ...

...n |first=Ronald |author2=Mauro Maggioni |title=Diffusion Wavelets |journal=Applied and Computational Harmonic Analysis |date=May 2008 |volume=24 |issue=3 |pag ...iffusion Processes|conference=The 23rd International Conference on Machine Learning|year=2006|url=http://www.cs.umass.edu/~mahadeva/papers/icml2006.pdf}}</ref> ...

In [[probability theory]], [[statistics]], and [[machine learning]], the '''continuous Bernoulli distribution'''<ref>Loaiza-Ganem, G., & Cunn ...using a learned similarity metric. In International conference on machine learning (pp. 1558-1566).</ref><ref>Jiang, Z., Zheng, Y., Tan, H., Tang, B., & Zhou, ...

{{short description|Machine learning method}} ...e process of creating multiple models (typically [[Neural network (machine learning)|artificial neural networks]]) and combining them to produce a desired outp ...

...input sensory images, usually some extracting features of images. Through learning, some hypothesis of the next action are given and according to the probabil Geometric feature learning methods extract distinctive geometric features from images. Geometric featu ...

A maximum likelihood estimation can be applied: ...-Maximization Algorithm (EM) |url=https://medium.com/@jonathan_hui/machine-learning-expectation-maximization-algorithm-em-2e954cb76959 |website=Medium |languag ...

{{Machine learning bar}} Since the range of values of raw data varies widely, in some [[machine learning]] algorithms, objective functions will not work properly without [[Normaliz ...

...een the balls. The covering number quantifies the size of a set and can be applied to general [[Metric space|metric spaces]]. Two related concepts are the ''p == Application to machine learning == ...

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

{{machine learning bar}} ...model|generative]] [[graphical model]], or alternatively a class of [[deep learning|deep]] [[artificial neural network|neural network]], composed of multiple l ...

{{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]] ...