Search results for "Discriminant function" - Rhea

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• Discriminant Function Old Kiwi
  ...ots, w_k</math> and feature vector x, which is in n-dimensional space, the discriminant functions <math>g_1(x), g_2(x), \ldots, g_k(x)</math> where <math>g_\#(x)</ Discriminant functions are used to define [[Decision Surfaces_Old Kiwi]].
  548 B (89 words) - 09:53, 10 April 2008
• Discriminant function Old Kiwi
  #REDIRECT: [[discriminant Function_Old Kiwi]]
  45 B (5 words) - 11:54, 17 March 2008

Page text matches

• ECE662:BoutinSpring08 Old Kiwi
  * [[Lecture 5 - Discriminant Functions_Old Kiwi]] * [[Lecture 6 - Discriminant Functions_Old Kiwi]]
  6 KB (747 words) - 05:18, 5 April 2013
• ECE662:Glossary Old Kiwi
  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 (4,832 words) - 18:13, 22 October 2010
• Lecture 17 - Nearest Neighbors Clarification Rule and Metrics Old Kiwi
  [[Lecture 5 - Discriminant Functions_Old Kiwi|5]], [[Lecture 6 - Discriminant Functions_Old Kiwi|6]],
  6 KB (938 words) - 08:38, 17 January 2013
• Lecture 1 - Introduction Old Kiwi
  [[Lecture 5 - Discriminant Functions_Old Kiwi|5]], [[Lecture 6 - Discriminant Functions_Old Kiwi|6]],
  3 KB (468 words) - 08:45, 17 January 2013
• Lecture 2 - Decision Hypersurfaces Old Kiwi
  [[Lecture 5 - Discriminant Functions_Old Kiwi|5]], [[Lecture 6 - Discriminant Functions_Old Kiwi|6]],
  5 KB (737 words) - 08:45, 17 January 2013
• Lecture 3 - Bayes classification Old Kiwi
  [[Lecture 5 - Discriminant Functions_Old Kiwi|5]], [[Lecture 6 - Discriminant Functions_Old Kiwi|6]],
  5 KB (843 words) - 08:46, 17 January 2013
• Lecture 5 - Discriminant Functions Old Kiwi
  [[Lecture 5 - Discriminant Functions_Old Kiwi|5]], [[Lecture 6 - Discriminant Functions_Old Kiwi|6]],
  6 KB (916 words) - 08:47, 17 January 2013
• Lecture 6 - Discriminant Functions Old Kiwi
  [[Lecture 5 - Discriminant Functions_Old Kiwi|5]], [[Lecture 6 - Discriminant Functions_Old Kiwi|6]],
  9 KB (1,586 words) - 08:47, 17 January 2013
• Lecture 7 - MLE and BPE Old Kiwi
  [[Lecture 5 - Discriminant Functions_Old Kiwi|5]], [[Lecture 6 - Discriminant Functions_Old Kiwi|6]],
  10 KB (1,488 words) - 10:16, 20 May 2013
• Lecture 8 - MLE, BPE and Linear Discriminant Functions Old Kiwi
  [[Lecture 5 - Discriminant Functions_Old Kiwi|5]], [[Lecture 6 - Discriminant Functions_Old Kiwi|6]],
  5 KB (792 words) - 08:48, 17 January 2013
• Lecture 9 - Linear Discriminant Functions Old Kiwi
  [[Lecture 5 - Discriminant Functions_Old Kiwi|5]], [[Lecture 6 - Discriminant Functions_Old Kiwi|6]],
  8 KB (1,307 words) - 08:48, 17 January 2013
• Lecture 10 - Batch Perceptron and Fisher Linear Discriminant Old Kiwi
  [[Lecture 5 - Discriminant Functions_Old Kiwi|5]], [[Lecture 6 - Discriminant Functions_Old Kiwi|6]],
  5 KB (755 words) - 08:48, 17 January 2013
• Lecture 11 - Fischer's Linear Discriminant again Old Kiwi
  [[Lecture 5 - Discriminant Functions_Old Kiwi|5]], [[Lecture 6 - Discriminant Functions_Old Kiwi|6]],
  5 KB (907 words) - 08:49, 17 January 2013
• Lecture 12 - Support Vector Machine and Quadratic Optimization Problem Old Kiwi
  [[Lecture 5 - Discriminant Functions_Old Kiwi|5]], [[Lecture 6 - Discriminant Functions_Old Kiwi|6]],
  8 KB (1,235 words) - 08:49, 17 January 2013
• Lecture 13 - Kernel function for SVMs and ANNs introduction Old Kiwi
  [[Lecture 5 - Discriminant Functions_Old Kiwi|5]], [[Lecture 6 - Discriminant Functions_Old Kiwi|6]],
  8 KB (1,354 words) - 08:51, 17 January 2013
• Lecture 14 - ANNs, Non-parametric Density Estimation (Parzen Window) Old Kiwi
  [[Lecture 5 - Discriminant Functions_Old Kiwi|5]], [[Lecture 6 - Discriminant Functions_Old Kiwi|6]],
  13 KB (2,073 words) - 08:39, 17 January 2013
• Lecture 15 - Parzen Window Method Old Kiwi
  [[Lecture 5 - Discriminant Functions_Old Kiwi|5]], [[Lecture 6 - Discriminant Functions_Old Kiwi|6]],
  7 KB (1,212 words) - 08:38, 17 January 2013
• Lecture 16 - Parzen Window Method and K-nearest Neighbor Density Estimate Old Kiwi
  [[Lecture 5 - Discriminant Functions_Old Kiwi|5]], [[Lecture 6 - Discriminant Functions_Old Kiwi|6]],
  10 KB (1,607 words) - 08:38, 17 January 2013
• Lecture 18 - Nearest Neighbors Clarification Rule and Metrics(Continued) Old Kiwi
  [[Lecture 5 - Discriminant Functions_Old Kiwi|5]], [[Lecture 6 - Discriminant Functions_Old Kiwi|6]],
  6 KB (1,066 words) - 08:40, 17 January 2013
• Support Vector Machines Old Kiwi
  ...ratic Optimization Problem_Old Kiwi|Lecture 12]] and [[Lecture 13 - Kernel function for SVMs and ANNs introduction_Old Kiwi|Lecture 13]]. ...an, E.M. Braverman, L.I. Rozoner. Theoretical foundations of the potential function method in pattern recognition learning. Automation and Control, 1964, Vol.
  3 KB (366 words) - 08:48, 10 April 2008
• Linear discriminant functions Old Kiwi
  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
• Publications Old Kiwi
  ...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 4 - Bayes Classification Old Kiwi
  [[Lecture 5 - Discriminant Functions_Old Kiwi|5]], [[Lecture 6 - Discriminant Functions_Old Kiwi|6]],
  8 KB (1,360 words) - 08:46, 17 January 2013
• Linear Discriminant Functions Old Kiwi
  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 Linear Discriminant Old Kiwi
  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 19 - Nearest Neighbor Error Rates Old Kiwi
  [[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 20 - Density Estimation using Series Expansion and Decision Trees Old Kiwi
  [[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 21 - Decision Trees(Continued) Old Kiwi
  [[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 22 - Decision Trees and Clustering Old Kiwi
  [[Lecture 5 - Discriminant Functions_Old Kiwi|5]], [[Lecture 6 - Discriminant Functions_Old Kiwi|6]],
  6 KB (806 words) - 08:42, 17 January 2013
• Naive Bayes Old Kiwi
  ...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 23 - Spanning Trees Old Kiwi
  [[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 24 - Clustering and Hierarchical Clustering Old Kiwi
  [[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 25 - Clustering Algorithms Old Kiwi
  [[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 26 - Statistical Clustering Methods Old Kiwi
  [[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 27 - Clustering by finding valleys of densities Old Kiwi
  [[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 28 - Final lecture Old Kiwi
  [[Lecture 5 - Discriminant Functions_Old Kiwi|5]], [[Lecture 6 - Discriminant Functions_Old Kiwi|6]],
  10 KB (1,728 words) - 08:55, 17 January 2013
• Recommendations for nonlinear discrimination methods Old Kiwi
  #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 err
  2 KB (311 words) - 10:49, 26 April 2008
• Lecture 2 - Decision Hypersurfaces OldKiwi
  [[Lecture 5 - Discriminant Functions_OldKiwi|5]]| [[Lecture 6 - Discriminant Functions_OldKiwi|6]]|
  5 KB (744 words) - 11:17, 10 June 2013
• ECE662:BoutinSpring08 OldKiwi
  * [[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 9 - Linear Discriminant Functions OldKiwi
  [[Lecture 5 - Discriminant Functions_OldKiwi|5]]| [[Lecture 6 - Discriminant Functions_OldKiwi|6]]|
  9 KB (1,341 words) - 11:15, 10 June 2013
• Lecture21ECE662S10
  ...linked to Fisher's linear discriminant. We then introduced Fisher's linear discriminant.
  961 B (135 words) - 08:27, 12 April 2010
• Lecture24ECE662S10
  ...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
• Noteslecture20ECE662S10
  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
• Decision Theory Glossary ECE662Spring2008
  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
• Lecture2ECE662S12
  ...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 securit
  2 KB (358 words) - 12:30, 23 February 2012
• Lecture3ECE662S12
  * 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
• Lecture6ECE662S12
  ...(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
• Lecture8ECE662S12
  ...then noticed the presence of the Mahalanobis distance in the discriminant function, and derived the relationship between the Mahalanobis distance and the Eucl
  3 KB (355 words) - 12:31, 23 February 2012
• Lecture 1 - Introduction OldKiwi
  [[Lecture 5 - Discriminant Functions_OldKiwi|5]]| [[Lecture 6 - Discriminant Functions_OldKiwi|6]]|
  3 KB (413 words) - 11:17, 10 June 2013
• Lecture 3 - Bayes classification OldKiwi
  [[Lecture 5 - Discriminant Functions_OldKiwi|5]]| [[Lecture 6 - Discriminant Functions_OldKiwi|6]]|
  6 KB (874 words) - 11:17, 10 June 2013
• Lecture 4 - Bayes Classification OldKiwi
  [[Lecture 5 - Discriminant Functions_OldKiwi|5]]| [[Lecture 6 - Discriminant Functions_OldKiwi|6]]|
  8 KB (1,403 words) - 11:17, 10 June 2013
• Lecture 16 - Parzen Window Method and K-nearest Neighbor Density Estimate OldKiwi
  [[Lecture 5 - Discriminant Functions_OldKiwi|5]]| [[Lecture 6 - Discriminant Functions_OldKiwi|6]]|
  10 KB (1,609 words) - 11:22, 10 June 2013
• Lecture 17 - Nearest Neighbors Clarification Rule and Metrics OldKiwi
  [[Lecture 5 - Discriminant Functions_OldKiwi|5]]| [[Lecture 6 - Discriminant Functions_OldKiwi|6]]|
  6 KB (977 words) - 11:22, 10 June 2013
• Lecture 18 - Nearest Neighbors Clarification Rule and Metrics(Continued) OldKiwi
  [[Lecture 5 - Discriminant Functions_OldKiwi|5]]| [[Lecture 6 - Discriminant Functions_OldKiwi|6]]|
  7 KB (1,098 words) - 11:22, 10 June 2013
• Lecture 6 - Discriminant Functions OldKiwi
  [[Category:discriminant function]] [[Lecture 5 - Discriminant Functions_OldKiwi|5]]|
  10 KB (1,604 words) - 11:17, 10 June 2013
• Lecture 7 - MLE and BPE OldKiwi
  [[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
  [[Lecture 5 - Discriminant Functions_OldKiwi|5]]| [[Lecture 6 - Discriminant Functions_OldKiwi|6]]|
  6 KB (946 words) - 11:17, 10 June 2013
• Lecture 8 - MLE, BPE and Linear Discriminant Functions OldKiwi
  [[Lecture 5 - Discriminant Functions_OldKiwi|5]]| [[Lecture 6 - Discriminant Functions_OldKiwi|6]]|
  6 KB (833 words) - 11:16, 10 June 2013
• Lecture 10 - Batch Perceptron and Fisher Linear Discriminant OldKiwi
  [[Lecture 5 - Discriminant Functions_OldKiwi|5]]| [[Lecture 6 - Discriminant Functions_OldKiwi|6]]|
  6 KB (813 words) - 11:18, 10 June 2013
• Lecture 11 - Fischer's Linear Discriminant again OldKiwi
  [[Lecture 5 - Discriminant Functions_OldKiwi|5]]| [[Lecture 6 - Discriminant Functions_OldKiwi|6]]|
  6 KB (946 words) - 11:18, 10 June 2013
• Lecture 12 - Support Vector Machine and Quadratic Optimization Problem OldKiwi
  [[Lecture 5 - Discriminant Functions_OldKiwi|5]]| [[Lecture 6 - Discriminant Functions_OldKiwi|6]]|
  8 KB (1,278 words) - 11:19, 10 June 2013
• Lecture 13 - Kernel function for SVMs and ANNs introduction OldKiwi
  [[Lecture 5 - Discriminant Functions_OldKiwi|5]]| [[Lecture 6 - Discriminant Functions_OldKiwi|6]]|
  9 KB (1,389 words) - 11:19, 10 June 2013
• Lecture 14 - ANNs, Non-parametric Density Estimation (Parzen Window) OldKiwi
  [[Lecture 5 - Discriminant Functions_OldKiwi|5]]| [[Lecture 6 - Discriminant Functions_OldKiwi|6]]|
  13 KB (2,098 words) - 11:21, 10 June 2013
• Lecture 15 - Parzen Window Method OldKiwi
  [[Lecture 5 - Discriminant Functions_OldKiwi|5]]| [[Lecture 6 - Discriminant Functions_OldKiwi|6]]|
  8 KB (1,246 words) - 11:21, 10 June 2013
• Lecture 19 - Nearest Neighbor Error Rates OldKiwi
  [[Lecture 5 - Discriminant Functions_OldKiwi|5]]| [[Lecture 6 - Discriminant Functions_OldKiwi|6]]|
  6 KB (1,041 words) - 11:22, 10 June 2013
• Lecture 20 - Density Estimation using Series Expansion and Decision Trees OldKiwi
  [[Lecture 5 - Discriminant Functions_OldKiwi|5]]| [[Lecture 6 - Discriminant Functions_OldKiwi|6]]|
  7 KB (1,082 words) - 11:23, 10 June 2013
• Lecture 21 - Decision Trees(Continued) OldKiwi
  [[Lecture 5 - Discriminant Functions_OldKiwi|5]]| [[Lecture 6 - Discriminant Functions_OldKiwi|6]]|
  7 KB (1,055 words) - 11:23, 10 June 2013
• Lecture 22 - Decision Trees and Clustering OldKiwi
  [[Lecture 5 - Discriminant Functions_OldKiwi|5]]| [[Lecture 6 - Discriminant Functions_OldKiwi|6]]|
  6 KB (837 words) - 11:23, 10 June 2013
• Lecture 23 - Spanning Trees OldKiwi
  [[Lecture 5 - Discriminant Functions_OldKiwi|5]]| [[Lecture 6 - Discriminant Functions_OldKiwi|6]]|
  7 KB (1,091 words) - 11:23, 10 June 2013
• Lecture 24 - Clustering and Hierarchical Clustering OldKiwi
  [[Lecture 5 - Discriminant Functions_OldKiwi|5]]| [[Lecture 6 - Discriminant Functions_OldKiwi|6]]|
  9 KB (1,276 words) - 11:24, 10 June 2013
• Lecture 25 - Clustering Algorithms OldKiwi
  [[Lecture 5 - Discriminant Functions_OldKiwi|5]]| [[Lecture 6 - Discriminant Functions_OldKiwi|6]]|
  8 KB (1,299 words) - 11:24, 10 June 2013
• Lecture11ECE662S12
  ...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-di
  2 KB (269 words) - 12:20, 23 February 2012
• Lecture 26 - Statistical Clustering Methods OldKiwi
  [[Lecture 5 - Discriminant Functions_OldKiwi|5]]| [[Lecture 6 - Discriminant Functions_OldKiwi|6]]|
  8 KB (1,214 words) - 11:24, 10 June 2013
• Lecture 27 - Clustering by finding valleys of densities OldKiwi
  [[Lecture 5 - Discriminant Functions_OldKiwi|5]]| [[Lecture 6 - Discriminant Functions_OldKiwi|6]]|
  8 KB (1,313 words) - 11:24, 10 June 2013
• Lecture 28 - Final lecture OldKiwi
  [[Lecture 5 - Discriminant Functions_OldKiwi|5]]| [[Lecture 6 - Discriminant Functions_OldKiwi|6]]|
  10 KB (1,704 words) - 11:25, 10 June 2013
• Hw3 ECE662 S12
  Weighting Function= Adaptive Gaussian <br\> Discriminant Functions<br/>
  25 KB (2,524 words) - 07:19, 25 June 2012
• Discriminant Functions For The Normal(Gaussian) Density
  = Discriminant Functions For The Normal Density - Part 1 = &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;Before talking about discriminant functions for the normal density, we first need to know what a normal distr
  5 KB (844 words) - 05:43, 13 April 2013
• ECE662 Pattern Recognition Decision Making Processes Spring2008 sLecture collective
  * [[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(Gaussian) Density - Part 2
  = 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
• Student projects
  **[[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
• ECE600 F13 Joint Expectation mhossain
  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
• Bayes Rule for 1-dimensional and N-dimensional feature spaces
  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
• PCA
  ...n which maximizes this parameter. One such function is the ''Fisher linear discriminant''[4].
  22 KB (3,459 words) - 10:40, 22 January 2015
• Bayes Rule Minimize Risk Dennis Lee
  so the discriminant function becomes so the discriminant function becomes
  12 KB (1,810 words) - 10:46, 22 January 2015
• Bayes rule in practice
  ...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 as
  7 KB (1,177 words) - 10:47, 22 January 2015
• Slecture Bayes rule to minimize risk Andy Park review
  ...xample. Step by step, concepts like likelihood, posterior and discriminant function are introduced with graphs and numerical deviration. Finally, likelihood ra
  2 KB (303 words) - 09:59, 12 May 2014
• Bayes rule in practice review
  ...scenario under Gaussian assumption. First it derived the log-discriminant function according to Bayes rule. Next it introduced density estimation technique in
  2 KB (259 words) - 12:40, 2 May 2014
• Discussion about Discriminant Functions for the Multivariate Normal Density
  [[Category:Discriminant Funtions]] '''Discussion about Discriminant Functions for the Multivariate Normal Density''' <br />
  14 KB (2,287 words) - 10:46, 22 January 2015
• 662slecture tang
  and vise versa. Here <math>g({\mathbf{x}})</math> is the discriminant function. Then the discriminant function will be
  9 KB (1,382 words) - 10:47, 22 January 2015
• Parzen Window Density Estimation
  ...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
• Least Squares Support Vector Machine and its Applications in Solving Linear Regression Problems
  ...'w''₂</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> denote
  14 KB (2,241 words) - 10:56, 22 January 2015
• CBR logistic regression
  ** 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
• Classification using Bayes Rule in 1-dimensional and N-dimensional feature spaces Review
  ...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 dimensi
  2 KB (359 words) - 09:58, 3 May 2014
• LinearClassierSlectureJMS
  <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 reg
  10 KB (1,684 words) - 13:00, 5 May 2014
• JMSLinearClassifierSlecture
  <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 reg
  10 KB (1,666 words) - 10:56, 22 January 2015
• JMSLinearClassifierSlecture review
  ...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
• Project1 ECE662S16
  *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 classi
  3 KB (434 words) - 17:19, 1 February 2016