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- ...ives on support-vector machines''' provide a way of interpreting [[support-vector machine]]s (SVMs) in the context of other regularization-based machine-lear ...|last2=Wahba |author2-link=Grace Wahba |title=Multicategory Support Vector Machines |journal=Journal of the American Statistical Association |year=2012 |volume ...10 KB (1,465 words) - 08:02, 6 June 2024
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- |class=[[Optimization algorithm]] for training support vector machines ...ic programming]] (QP) problem that arises during the training of [[support-vector machine]]s (SVM). It was invented by [[John Platt (computer scientist)|John ...7 KB (1,038 words) - 20:30, 1 July 2023
- ...s {{math|''y'' < 1}}, corresponding to the notion of a margin in a support vector machine.]] ...ss is used for "maximum-margin" classification, most notably for [[support vector machine]]s (SVMs).<ref>{{Cite journal | last1 = Rosasco | first1 = L. | las ...8 KB (1,205 words) - 15:32, 9 August 2024
- ...Platt (computer scientist) |title=Probabilistic outputs for support vector machines and comparisons to regularized likelihood methods |journal=Advances in Larg ...or4=Sayan Mukherjee |title=Choosing multiple parameters for support vector machines |journal=Machine Learning |year=2002 |volume=46 |pages=131–159 |url=http:// ...7 KB (980 words) - 16:42, 18 February 2025
- ...zed]] learning algorithms. In particular, it is commonly used in [[support vector machine]] [[statistical classification|classification]].<ref name="Chang201 Because support vector machines and other models employing the [[kernel trick]] do not scale well to large ...7 KB (1,002 words) - 00:02, 29 September 2024
- ...ives on support-vector machines''' provide a way of interpreting [[support-vector machine]]s (SVMs) in the context of other regularization-based machine-lear ...|last2=Wahba |author2-link=Grace Wahba |title=Multicategory Support Vector Machines |journal=Journal of the American Statistical Association |year=2012 |volume ...10 KB (1,465 words) - 08:02, 6 June 2024
- ...es=25–30 |citeseerx=10.1.1.100.2524}}</ref> it can reduce the time to find support vectors. Feature scaling is also often used in applications involving dista ...value, <math>\bar{x}=\text{average}(x)</math> is the mean of that feature vector. There is another form of the means normalization which divides by the stan ...8 KB (1,131 words) - 02:18, 24 August 2024
- ..., without having to translate these to fixed-length, real-valued [[feature vector]]s.<ref name="Lodhi"/> String kernels are used in domains where sequence da | chapter = Improved Online Support Vector Machines Spam Filtering Using String Kernels ...7 KB (984 words) - 16:58, 22 August 2023
- :** Construct a new label vector {{mvar|z}} where {{math|''z''{{sub|''i''}} }} {{=}} {{math|''y''{{sub|''i'' ...[support vector machines]] and [[Extreme learning machine|extreme learning machines]] to address multi-class classification problems. These types of techniques ...12 KB (1,671 words) - 19:16, 29 January 2025
- ...walle.<ref>Suykens, J. A. K.; Vandewalle, J. (1999) "Least squares support vector machine classifiers", ''Neural Processing Letters'', 9 (3), 293–300.</ref> ==From support-vector machine to least-squares support-vector machine== ...16 KB (2,683 words) - 07:10, 22 May 2024
- ...learning]] algorithm that generalizes the [[support-vector machine|Support-Vector Machine]] (SVM) classifier. Whereas the SVM classifier supports [[binary c ...{Y} \to \mathbb{R}^d</math> is a feature function, extracting some feature vector from a given sample and label. The design of this function depends very mu ...7 KB (1,102 words) - 10:50, 29 January 2023
- ...essing type|the I/O method|vectored I/O|register gather/scatter, part of [[vector processing]]|permute instruction}} ...[[Single Instruction Multiple Data|SIMD]] units in [[CPU]]s) have hardware support for gather and scatter operations, as do many [[input/output]] systems, all ...8 KB (1,151 words) - 18:39, 2 December 2023
- ...st2=Ji|last3=Zou|first3=Hui|date=2006|title=The doubly regularized support vector machine|journal=Statistica Sinica|volume=16|pages=589–615|url=http://www.st == Reduction to support vector machine == ...12 KB (1,714 words) - 00:10, 29 January 2025
- ...lar [[perceptron]] learning algorithm that can learn [[kernel trick|kernel machines]], i.e. non-linear [[statistical classification|classifiers]] that employ a ...th|''b''}}, omitted here for simplicity) that is used to classify a sample vector {{math|'''x'''}} as class "one" or class "minus one" according to ...9 KB (1,362 words) - 23:03, 5 May 2021
- ...und]] in [[Boosting (machine learning)|boosting]] algorithms and [[support vector machine]]s is particularly prominent. ...4 KB (629 words) - 22:28, 3 November 2024
- ...ss function]]) are naturally probabilistic. Other models such as [[support vector machine]]s are not, but [[#Probability calibration|methods exist]] to turn ...ility distribution or the "signed distance to the hyperplane" in a support vector machine). Deviations from the identity function indicate a poorly-calibrat ...11 KB (1,470 words) - 19:54, 17 January 2024
- * [[Structured support vector machine]]s ...ining sample <math>x</math> and a candidate prediction <math>y</math> to a vector of length <math>n</math> (<math>x</math> and <math>y</math> may have any st ...6 KB (897 words) - 21:14, 1 February 2025
- }}</ref> Then, if <math>\mathbf{f}</math> is a vector of the values of <math>f</math> at the data, <math>\mathbf{f} = [f(x_1), \l ...ASSO and [[elastic net regularization]] can be expressed as support vector machines.<ref>{{cite book ...28 KB (3,958 words) - 22:50, 27 February 2024
- ...bb{R}^k</math>, ''k'' < ''p'', thereby reducing the [[dimension (vector space)|dimension]] of <math>\textbf{x}</math>.<ref name="Cook & Adragni:200 [[Without loss of generality]], only the [[vector space|space]] [[linear span|spanned]] by the columns of <math>A</math> need ...12 KB (1,824 words) - 00:36, 15 May 2024
- In [[machine learning]], a '''ranking SVM''' is a variant of the [[support vector machine]] algorithm, which is used to solve certain [[ranking]] problems (v ===SVM classifier<ref>C. Cortes and V.N. Vapnik. "Support-vector networks." ''Machine Learning Journal'', 20: 273–297,1995</ref>=== ...12 KB (1,993 words) - 08:55, 11 December 2023
- ...ject data points into a high-dimensional or infinite-dimensional [[Feature vector|feature space]] and find the optimal splitting hyperplane. In the [[kernel In a vector and kernel notation, the problem of [[regularized least squares]] can be re ...14 KB (2,145 words) - 18:06, 20 January 2025