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| known_for = textbooks on [[statistical learning theory]] and [[support vector machines]] ...Nello Cristianini have become standard monographs for the study of kernel methods and support vector machines and together have attracted 21000 citations. He ...

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

| known_for = [[Statistical learning]], [[media content analysis]], [[support vector machines]], [[Philosophy of ...''[[An Introduction to Support Vector Machines]]'' and ''[[Kernel Methods for Pattern Analysis]]'' and a book ...

{{Short description|Class of algorithms for pattern analysis}} {{Machine learning bar}} ...

...8300017467 | volume=10 | issue=5 | title=Nonlinear Component Analysis as a Kernel Eigenvalue Problem | year=1998 | journal=Neural Computation | pages=1299–13 ..., the originally linear operations of PCA are performed in a [[reproducing kernel Hilbert space]]. ...

{{short description|Statistical classification in machine learning}} ...practical problems such as [[document classification]], and more generally for problems with many variables ([[feature vector|features]]), reaching accura ...

* [[Machine Learning]] * [[Kernel Methods]] ...

{{Short description|Method in machine learning}} ...Early stopping rules have been employed in many different machine learning methods, with varying amounts of theoretical foundation. ...

[[Wikipedia:WikiProject Outlines]] for more details. ...o the [[humanities]]; it is also used and [[Misuse of statistics|misused]] for making informed decisions in all areas of [[business]] and [[government]]. ...

...ty,<ref>{{Harvnb|Lawrence|2012|loc=page 1612}}</ref> as it leverages the [[Kernel trick]] to non-linearly map the original data into an [[inner-product space ...graph is "unfolded" with the help of semidefinite programming. Instead of learning the output vectors directly, the semidefinite programming aims to find an i ...

...rent [[false positive and false negative|types of errors]] is of interest. For example, in medical testing, detecting a disease when it is not present (a ...call]]. These are the proportion of the ''population with the condition'' for which the test is correct. ...

{{Short description|Data mining for patterns in molecule data}} {{Cleanup|reason=The article is just lists. The reason for each list needs an introduction |date=September 2024}} ...

{{short description|Framework for machine learning}} ...statistical learning in machine learning|its use in psychology|Statistical learning in language acquisition}} ...

When [[classification]] is performed by a computer, statistical methods are normally used to develop the algorithm. ...variously be [[categorical data|categorical]] (e.g. "A", "B", "AB" or "O", for [[blood type]]), [[ordinal data|ordinal]] (e.g. "large", "medium" or "small ...

...lbert Spaces |date=2008 |work=Lecture notes of CS281B/Stat241B Statistical Learning Theory |publisher=University of California at Berkeley |url=https://people. ...e first consider an important special case; see [[#Generalizations|below]] for a more general formulation. ...

{{Short description|Machine learning paradigm}} ...raining data is labeled with the expected answers, while in [[unsupervised learning]], the model identifies patterns or structures in unlabeled data.]] ...

==Direct methods== ==Indirect methods== ...

...8e|date=2020-04-06}} &nbsp;&nbsp; • [[Kenneth L. Judd]], 1998. ''Numerical Methods in Economics'', MIT Press. Links to [http://mitpress.mit.edu/catalog/item/d Computational methods have been applied in various fields of economics research, including but no ...

...rowth of the use of [[semi-structured data]] has created new opportunities for data mining, which has traditionally been concerned with tabular data sets, ...e normally very sophisticated, containing multiple optional subtrees, used for representing special case data. Frequently around 90% of a schema is concer ...

{{For|dimensional reduction in physics|dimensional reduction}} ...trinsic dimension]]. Working in high-dimensional spaces can be undesirable for many reasons; raw data are often [[sparse matrix|sparse]] as a consequence ...