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{{Short description|Machine learning kernel function}} ...= http://jmlr.org/papers/v11/chang10a.html | journal = Journal of Machine Learning Research | volume = 11 | pages = 1471–1490 }}</ref> ...

{{Short description|Machine learning kernel function}} {{about|machine learning|polynomial kernels in complexity theory|Kernelization}} ...

{{machine learning bar}} ...classifiers with online and active learning |journal=[[Journal of Machine Learning Research|JMLR]] |volume=6 |year=2005 |pages=1579–1619}}</ref> ...

...similar two strings ''a'' and ''b'' are, the higher the value of a string kernel ''K''(''a'', ''b'') will be. ...h [[Kernel trick|kernelized]] learning algorithms such as [[support vector machine]]s allow such algorithms to work with strings, without having to translate ...

...too large for traditional kernel methods like [[support vector machine]], kernel [[ridge regression]], and [[gaussian process]]. === Kernel method === ...

...dan |first2=Michael I. |title=Predictive low-rank decomposition for kernel methods |url=http://portal.acm.org/citation.cfm?doid=1102351.1102356 |language=en | ...' methods. Both of them have been successfully applied to efficient kernel learning. ...

{{short description|Set of machine learning methods}} {{Machine learning bar}} ...

...mple sparsity or smoothness, that can produce stable predictive functions. For example, in the more common vector framework, [[Tikhonov regularization]] o ...nd these can be generalized to the nonparametric case of [[multiple kernel learning]]. ...

...used to reduce the impact of noise and prevent overfitting when a machine learning system is being trained on a labeled set of emails to learn how to tell a s ...TSVD). As for choosing the regularization parameter, examples of candidate methods to compute this parameter include the discrepancy principle, generalized [[ ...

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

...icio A.|author2=Rosasco, Lorenzo |author3=Lawrence, Neil D. |title=Kernels for Vector-Valued Functions: A Review|eprint=1106.6251 |date=June 2011|class=st ...with a brief review of the main ideas underlying kernel methods for scalar learning, and briefly introduce the concepts of regularization and Gaussian processe ...

|class=[[Optimization algorithm]] for training support vector machines ...ramming]] (QP) problem that arises during the training of [[support-vector machine]]s (SVM). It was invented by [[John Platt (computer scientist)|John Platt]] ...

{{Short description|Statement in computational learning theory}} ...ical motivations for the use of non-linear [[kernel methods]] in [[machine learning]] applications. It is so termed after the information theorist [[Thomas M. ...

...the geometry of a given diffusion operator <math>T</math> (e.g., a [[heat kernel]] or a [[random walk]]). Moreover, the diffusion wavelet basis functions ar ...iffusion Processes|conference=The 23rd International Conference on Machine Learning|year=2006|url=http://www.cs.umass.edu/~mahadeva/papers/icml2006.pdf}}</ref> ...

{{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|Machine learning problem}} {{machine learning bar}} ...

...tput of a function. Kernels encapsulate the properties of functions in a [[Kernel trick|computationally efficient]] way and allow algorithms to easily swap f ...-output learning or vector-valued learning), [[Inductive transfer|transfer learning]], and co-[[kriging]]. [[Multi-label classification]] can be interpreted as ...

...tor machine]]s (SVMs) in the context of other regularization-based machine-learning algorithms. SVM algorithms categorize binary data, with the goal of fittin ...SVM and other forms of Tikhonov regularization, and theoretical grounding for why it is beneficial to use SVM's loss function, the hinge loss.<ref name=" ...

...thods'' generalize and extend sparsity regularization methods, by allowing for optimal selection over structures like groups or networks of input variable ...asso|journal = Proceedings of the 26th International Conference on Machine Learning|year = 2009|display-authors=etal}}</ref> ...

...input sensory images, usually some extracting features of images. Through learning, some hypothesis of the next action are given and according to the probabil ...n be detected by [[feature detection (computer vision)|feature detection]] methods. ...