Create the page "Discriminant function" on this wiki! See also the search results found.
- [[Lecture 5 - Discriminant Functions_OldKiwi|5]]| [[Lecture 6 - Discriminant Functions_OldKiwi|6]]|8 KB (1,403 words) - 11:17, 10 June 2013
- [[Lecture 5 - Discriminant Functions_OldKiwi|5]]| [[Lecture 6 - Discriminant Functions_OldKiwi|6]]|10 KB (1,609 words) - 11:22, 10 June 2013
- [[Lecture 5 - Discriminant Functions_OldKiwi|5]]| [[Lecture 6 - Discriminant Functions_OldKiwi|6]]|6 KB (977 words) - 11:22, 10 June 2013
- [[Lecture 5 - Discriminant Functions_OldKiwi|5]]| [[Lecture 6 - Discriminant Functions_OldKiwi|6]]|7 KB (1,098 words) - 11:22, 10 June 2013
- [[Category:discriminant function]] [[Lecture 5 - Discriminant Functions_OldKiwi|5]]|10 KB (1,604 words) - 11:17, 10 June 2013
- [[Lecture 5 - Discriminant Functions_OldKiwi|5]]| [[Lecture 6 - Discriminant Functions_OldKiwi|6]]|10 KB (1,472 words) - 11:16, 10 June 2013
- [[Lecture 5 - Discriminant Functions_OldKiwi|5]]| [[Lecture 6 - Discriminant Functions_OldKiwi|6]]|6 KB (946 words) - 11:17, 10 June 2013
- [[Lecture 5 - Discriminant Functions_OldKiwi|5]]| [[Lecture 6 - Discriminant Functions_OldKiwi|6]]|6 KB (833 words) - 11:16, 10 June 2013
- [[Lecture 5 - Discriminant Functions_OldKiwi|5]]| [[Lecture 6 - Discriminant Functions_OldKiwi|6]]|6 KB (813 words) - 11:18, 10 June 2013
- [[Lecture 5 - Discriminant Functions_OldKiwi|5]]| [[Lecture 6 - Discriminant Functions_OldKiwi|6]]|6 KB (946 words) - 11:18, 10 June 2013
- [[Lecture 5 - Discriminant Functions_OldKiwi|5]]| [[Lecture 6 - Discriminant Functions_OldKiwi|6]]|8 KB (1,278 words) - 11:19, 10 June 2013
- [[Lecture 5 - Discriminant Functions_OldKiwi|5]]| [[Lecture 6 - Discriminant Functions_OldKiwi|6]]|9 KB (1,389 words) - 11:19, 10 June 2013
- [[Lecture 5 - Discriminant Functions_OldKiwi|5]]| [[Lecture 6 - Discriminant Functions_OldKiwi|6]]|13 KB (2,098 words) - 11:21, 10 June 2013
- [[Lecture 5 - Discriminant Functions_OldKiwi|5]]| [[Lecture 6 - Discriminant Functions_OldKiwi|6]]|8 KB (1,246 words) - 11:21, 10 June 2013
- [[Lecture 5 - Discriminant Functions_OldKiwi|5]]| [[Lecture 6 - Discriminant Functions_OldKiwi|6]]|6 KB (1,041 words) - 11:22, 10 June 2013
- [[Lecture 5 - Discriminant Functions_OldKiwi|5]]| [[Lecture 6 - Discriminant Functions_OldKiwi|6]]|7 KB (1,082 words) - 11:23, 10 June 2013
- [[Lecture 5 - Discriminant Functions_OldKiwi|5]]| [[Lecture 6 - Discriminant Functions_OldKiwi|6]]|7 KB (1,055 words) - 11:23, 10 June 2013
- [[Lecture 5 - Discriminant Functions_OldKiwi|5]]| [[Lecture 6 - Discriminant Functions_OldKiwi|6]]|6 KB (837 words) - 11:23, 10 June 2013
- [[Lecture 5 - Discriminant Functions_OldKiwi|5]]| [[Lecture 6 - Discriminant Functions_OldKiwi|6]]|7 KB (1,091 words) - 11:23, 10 June 2013
- [[Lecture 5 - Discriminant Functions_OldKiwi|5]]| [[Lecture 6 - Discriminant Functions_OldKiwi|6]]|9 KB (1,276 words) - 11:24, 10 June 2013
- [[Lecture 5 - Discriminant Functions_OldKiwi|5]]| [[Lecture 6 - Discriminant Functions_OldKiwi|6]]|8 KB (1,299 words) - 11:24, 10 June 2013
- ...nging the integration variable from the feature vector to the discriminant function, we end up having to compute two 1D integrations, as opposed to two an n-di2 KB (269 words) - 12:20, 23 February 2012
- [[Lecture 5 - Discriminant Functions_OldKiwi|5]]| [[Lecture 6 - Discriminant Functions_OldKiwi|6]]|8 KB (1,214 words) - 11:24, 10 June 2013
- [[Lecture 5 - Discriminant Functions_OldKiwi|5]]| [[Lecture 6 - Discriminant Functions_OldKiwi|6]]|8 KB (1,313 words) - 11:24, 10 June 2013
- [[Lecture 5 - Discriminant Functions_OldKiwi|5]]| [[Lecture 6 - Discriminant Functions_OldKiwi|6]]|10 KB (1,704 words) - 11:25, 10 June 2013
- Weighting Function= Adaptive Gaussian <br\> Discriminant Functions<br/>25 KB (2,524 words) - 07:19, 25 June 2012
- = Discriminant Functions For The Normal Density - Part 1 = Before talking about discriminant functions for the normal density, we first need to know what a normal distr5 KB (844 words) - 05:43, 13 April 2013
- * [[Lecture 5 - Discriminant Functions_OldKiwi|Lecture 5 - Discriminant Functions]] * [[Lecture 6 - Discriminant Functions_OldKiwi|Lecture 6 - Discriminant Functions]]3 KB (425 words) - 09:59, 4 November 2013
- = Discriminant Functions For The Normal Density - Part 2 = ...y''' and state of nature variable ''w'', we can represent the discriminant function as:11 KB (1,792 words) - 16:09, 19 April 2013
- **[[On Solving Cubic Function]] **[[Discriminant Functions For The Normal(Gaussian) Density|Discriminant Functions For The Normal(Gaussian) Density - Part 1]]3 KB (389 words) - 18:10, 23 February 2015
- which is a quadratic function of a ∈ '''R'''. Consider two cases: ...png|380px|thumb|left|Fig 1: A possible depiction of the quadratic when the discriminant is greater than zero.]]</center>7 KB (1,307 words) - 12:12, 21 May 2014
- where <math>\rho</math> is a density function for continuous values. That is, <math>\rho(x|\omega_i)</math> is a class-co ...de class <math>\omega_2</math>. Equivalently, we can define a discriminant function <math>g(x) = g_1(x) - g_2(x)</math> and decide class <math>\omega_1</math>19 KB (3,255 words) - 10:47, 22 January 2015
- ...n which maximizes this parameter. One such function is the ''Fisher linear discriminant''[4].22 KB (3,459 words) - 10:40, 22 January 2015
- so the discriminant function becomes so the discriminant function becomes12 KB (1,810 words) - 10:46, 22 January 2015
- ...class scenario under Gaussian assumption. We first derive the discriminant function according to Bayes rule. Then we introduce the density estimation methods i <br>For the two-class case, generate the discriminant function as7 KB (1,177 words) - 10:47, 22 January 2015
- ...xample. Step by step, concepts like likelihood, posterior and discriminant function are introduced with graphs and numerical deviration. Finally, likelihood ra2 KB (303 words) - 09:59, 12 May 2014
- ...scenario under Gaussian assumption. First it derived the log-discriminant function according to Bayes rule. Next it introduced density estimation technique in2 KB (259 words) - 12:40, 2 May 2014
- [[Category:Discriminant Funtions]] '''Discussion about Discriminant Functions for the Multivariate Normal Density''' <br />14 KB (2,287 words) - 10:46, 22 January 2015
- and vise versa. Here <math>g({\mathbf{x}})</math> is the discriminant function. Then the discriminant function will be9 KB (1,382 words) - 10:47, 22 January 2015
- ...ern recognition and classification we have primarily focused on the use of discriminant functions as a means of classifying data. That is, for a set of classes <ma ...ds to the value of <math>\vec{\theta}</math> that maximizes the likelihood function, i.e.:16 KB (2,703 words) - 10:54, 22 January 2015
- ...'w''<sub>2</sub></span>. If the data is linear seperable, the discriminate function can be written as ...0\}</math> and it divides the space into two, the sign of the discriminant function <math>f(\textbf{y}) = \textbf{c}\cdot \textbf{y} - \textbf{b}</math> denote14 KB (2,241 words) - 10:56, 22 January 2015
- ** Examples are Naive Bayes Classifier, Linear Discriminant Analysis. ...r combination of the feature and a constant and feeding it into a logistic function:9 KB (1,540 words) - 10:56, 22 January 2015
- ...nal Gaussian distribution. After that, the author derived the discriminant function when classifying 1 dimensional and N dimensional Gaussian distribution. * There should be a derivation of the discriminant function when it is the case of the general <math> \Sigma_i </math> in the N dimensi2 KB (359 words) - 09:58, 3 May 2014
- <font size="4">Linear Discriminant Analysis and Fisher's Linear Discriminant </font> Linear discriminant analysis is a technique that classifies two classes by drawing decision reg10 KB (1,684 words) - 13:00, 5 May 2014
- <font size="4">Linear Discriminant Analysis and Fisher's Linear Discriminant </font> Linear discriminant analysis is a technique that classifies two classes by drawing decision reg10 KB (1,666 words) - 10:56, 22 January 2015
- ...SLinearClassifierSlecture|Linear Discriminant Analysis and Fisher's Linear Discriminant]]''' by John Mulcahy-Stanislawczyk ...scriminant Analysis. The slecture then goes on to describe Fisher's Linear Discriminant and how it is used to classify two-class data.1 KB (166 words) - 02:18, 11 May 2014
- *You must write your own function to classify the data (discriminant g(x)). Do not copy other people's code and do not use any toolbox to classi3 KB (434 words) - 17:19, 1 February 2016