Create the page "Discriminant function" on this wiki! See also the search results found.
- Discriminant fuction that is a linear combination of the component x <br> ...wo-class problems; where teh ith problem is solvd by a linear discriminant function that separates points assigned to w_i from those not assigned to w_i<br>2 KB (433 words) - 23:11, 10 March 2008
- ...f aspects of such problems: providing a better definition of the objective function, feature ...rom the Journal of Multivariate Analysis on Bayesian Estimators for Normal Discriminant Functions===39 KB (5,715 words) - 10:52, 25 April 2008
- [[Lecture 5 - Discriminant Functions_Old Kiwi|5]], [[Lecture 6 - Discriminant Functions_Old Kiwi|6]],8 KB (1,360 words) - 08:46, 17 January 2013
- Discriminant fuction that is a linear combination of the component x <br> ...wo-class problems; where teh ith problem is solvd by a linear discriminant function that separates points assigned to w_i from those not assigned to w_i<br>2 KB (428 words) - 09:12, 7 April 2008
- Fisher's linear discriminant is a classification method that projects high-dimensional data onto a line ...vec{w}}^{T}{S}_{B}\vec{w}} {{\vec{w}}^{T}{S}_{W}\vec{w}}</math>, explicit function of <math>\vec{w}</math>3 KB (475 words) - 18:05, 28 March 2008
- [[Lecture 5 - Discriminant Functions_Old Kiwi|5]], [[Lecture 6 - Discriminant Functions_Old Kiwi|6]],5 KB (1,003 words) - 08:40, 17 January 2013
- [[Lecture 5 - Discriminant Functions_Old Kiwi|5]], [[Lecture 6 - Discriminant Functions_Old Kiwi|6]],6 KB (1,047 words) - 08:42, 17 January 2013
- [[Lecture 5 - Discriminant Functions_Old Kiwi|5]], [[Lecture 6 - Discriminant Functions_Old Kiwi|6]],6 KB (1,012 words) - 08:42, 17 January 2013
- [[Lecture 5 - Discriminant Functions_Old Kiwi|5]], [[Lecture 6 - Discriminant Functions_Old Kiwi|6]],6 KB (806 words) - 08:42, 17 January 2013
- ...n with Gaussian class models will give the same results as Fisher's Linear Discriminant when the dimensions are independent. It may give results that are very clo function [data labels] = loadIris()3 KB (448 words) - 10:38, 22 April 2008
- [[Lecture 5 - Discriminant Functions_Old Kiwi|5]], [[Lecture 6 - Discriminant Functions_Old Kiwi|6]],7 KB (1,060 words) - 08:43, 17 January 2013
- [[Lecture 5 - Discriminant Functions_Old Kiwi|5]], [[Lecture 6 - Discriminant Functions_Old Kiwi|6]],8 KB (1,254 words) - 08:43, 17 January 2013
- [[Lecture 5 - Discriminant Functions_Old Kiwi|5]], [[Lecture 6 - Discriminant Functions_Old Kiwi|6]],8 KB (1,259 words) - 08:43, 17 January 2013
- [[Lecture 5 - Discriminant Functions_Old Kiwi|5]], [[Lecture 6 - Discriminant Functions_Old Kiwi|6]],8 KB (1,244 words) - 08:44, 17 January 2013
- [[Lecture 5 - Discriminant Functions_Old Kiwi|5]], [[Lecture 6 - Discriminant Functions_Old Kiwi|6]],8 KB (1,337 words) - 08:44, 17 January 2013
- [[Lecture 5 - Discriminant Functions_Old Kiwi|5]], [[Lecture 6 - Discriminant Functions_Old Kiwi|6]],10 KB (1,728 words) - 08:55, 17 January 2013
- #Try simple pattern recognition technique (K-nearest neighbor, linear discriminant analysis)first as a baseline before trying Neural network. #Use a radial basis function RBF (unsupervied selection of centers, weights optimized using a square err2 KB (311 words) - 10:49, 26 April 2008
- [[Lecture 5 - Discriminant Functions_OldKiwi|5]]| [[Lecture 6 - Discriminant Functions_OldKiwi|6]]|5 KB (744 words) - 11:17, 10 June 2013
- * [[Lecture 5 - Discriminant Functions_OldKiwi|Lecture 5 - Discriminant Functions]] * [[Lecture 6 - Discriminant Functions_OldKiwi|Lecture 6 - Discriminant Functions]]7 KB (875 words) - 07:11, 13 February 2012
- [[Lecture 5 - Discriminant Functions_OldKiwi|5]]| [[Lecture 6 - Discriminant Functions_OldKiwi|6]]|9 KB (1,341 words) - 11:15, 10 June 2013
- ...linked to Fisher's linear discriminant. We then introduced Fisher's linear discriminant.961 B (135 words) - 08:27, 12 April 2010
- ...the existence of en underlying feature space extension for a given kernel function. ...eresting page]] on the use of [[Fisher_Linear_Discriminant|Fisher's linear discriminant]] when the data is not linearly separable.1 KB (188 words) - 10:36, 16 April 2010
- Thursday, April 8th 2010<br>(Continuation of the linear discriminant of [[Lecture19ECE662S10|lecture 19]])<br> ...in \mathcal{D} \right | \vec{c} \cdot \vec{y}_{1} \le 0</math> be the cost function. Hard to minimize !<br>8 KB (1,247 words) - 09:25, 11 May 2010
- Bayes' decision rule creates an objective function which minimizes the probability of error (misclassification). This method a Refers to the problem caused by exponential growth of hypervolume as a function of dimensionality. This term was coined by Richard Bellman in 1961.31 KB (4,787 words) - 18:21, 22 October 2010
- ...o our toy example to illustrate the concepts of "decision boundaries" and "discriminant functions". Our example only included one feature (hair length), so the cor ...t would be silly to attack such a problem by trying to find a discriminant function. It is much better to look up all the names (e.g., on the US social securit2 KB (358 words) - 12:30, 23 February 2012
- * finding a discriminant function (i.e., a real-valued function whose domain is the feature space); ...aic varieties, with an emphasis on hyperplanes (i.e. when the discriminant function is a degree one polynomial).2 KB (243 words) - 12:30, 23 February 2012
- ...(in the usual, Euclidean sense), the larger the value of the discriminant function <math>g_i(x)</math> for that class.2 KB (298 words) - 12:31, 23 February 2012
- ...then noticed the presence of the Mahalanobis distance in the discriminant function, and derived the relationship between the Mahalanobis distance and the Eucl3 KB (355 words) - 12:31, 23 February 2012
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- [[Category:discriminant function]] [[Lecture 5 - Discriminant Functions_OldKiwi|5]]|10 KB (1,604 words) - 11:17, 10 June 2013
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