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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 [[ ...problems with a high imbalance in class distribution and a high imbalance in associated costs ...

{{Short description|Form of decision making in machine learning}} ...m of decision making using the concept of piecewise-linear separability of datasets to categorize data. ...

...ataset is imbalanced. It exploits the structures of conditional imbalanced datasets more efficiently than alternative methods, such as [[Logistic regression#Ca == Imbalanced datasets == ...

{{Short description|Subfield of machine learning}} .../ref> Preference learning typically involves [[supervised learning]] using datasets of pairwise preference comparisons, rankings, or other preference informati ...

{{Short description|Supervised learning of a similarity function}} ...|similar]] or related two objects are. It has applications in [[ranking]], in [[recommendation systems]], visual identity tracking, face verification, an ...

{{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 ...

...ts that are too large for traditional kernel methods like [[support vector machine]], kernel [[ridge regression]], and [[gaussian process]]. ...): \R^d \times \R^d \to \R</math>Kernel methods replaces linear operations in high-dimensional space by operations on the kernel matrix: <math display="b ...

{{Short description|Mathematical metric in normed vector space}} ...distance''' or '''Minkowski metric''' is a [[Metric (mathematics)|metric]] in a [[normed vector space]] which can be considered as a generalization of bo ...

...originally developed by Edward Rosten and Tom Drummond, and was published in 2006.<ref> |chapter=Machine Learning for High-speed Corner Detection ...

...omment|1=This article was possibly generated by AI without proper sourcing in certain sections. [[User:LR.127|LR.127]] ([[User talk:LR.127|talk]]) 01:59, {{Short description|An area of machine learning}} ...

{{Short description|Machine learning strategy}} ...machine learning method|active learning in the context of education|active learning}} ...

{{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]] ...

{{Machine learning}} ...ge Data Augmentation for Deep Learning | journal=Mathematics and Computers in Simulation | publisher=springer | volume=6 | year=2019 | doi=10.1186/s40537 ...

{{Short description|Approach in generative models}} ...[[Machine learning|learning from data]]. The approach prominently appears in [[generative artificial intelligence]]. ...

...|neural networks]] are trained using large amounts of recorded speech and, in the case of a text-to-speech system, the associated labels and/or input tex ...be [[Normal distribution|Gaussian]] or [[Laplace distribution|Laplacian]]. In practice, since the human voice band ranges from approximately 300 to 4000& ...

...which people's eyes focus first or the most relevant regions for [[machine learning model]]s.<ref>{{cite news |last1=Subhash |first1=Bijil |title=Explainable A ...o they should be highlighted on the saliency map. Saliency maps engineered in artificial or computer vision are typically not the same as the actual [[V1 ...

...ocessing (ICASSP)|chapter= Local principal component pursuit for nonlinear datasets|pages= 3925–3928|year=2012|doi= 10.1109/ICASSP.2012.6288776|isbn= 978-1-467 ...ion of the difference of the input matrix and the low-rank matrix obtained in the previous step, and iterating the two steps until [[Convergence (logic)| ...

...nference on Neural Information Processing Systems (NIPS) |series=Advances in Neural Information Processing Systems |date=Dec 2017 |volume=30 |publisher= ...r two are children of two green-card-carrying Germans who were temporarily in California and a first-generation American whose family had fled persecutio ...

...d both in statistical sampling, survey design methodology and in [[machine learning]]. ...rom another, to compensate for an imbalance that is either already present in the data, or likely to develop if a purely random sample were taken. Data I ...

...9 (3), 293–300.</ref> LS-SVMs are a class of [[Kernel methods|kernel-based learning methods]]. ==From support-vector machine to least-squares support-vector machine== ...