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- [[Category:decision theory]] =Bayes Decision Rule Video=1 KB (172 words) - 11:08, 10 June 2013
- == Proof of the Optimality of Bayes' Decision Rule ==774 B (101 words) - 10:43, 22 January 2015
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- ...Spring 2008 edition of the course [[ECE662|ECE662: Pattern Recognition and Decision Making processes]]. * [[Lecture 2 - Decision Hypersurfaces_Old Kiwi]]6 KB (747 words) - 05:18, 5 April 2013
- =Glossary for "Decision Theory" ([[ECE662]])= == [[Bayes Decision Rule_Old Kiwi|Bayes Decision Rule]] ==31 KB (4,832 words) - 18:13, 22 October 2010
- ...er program that classifies the feature vectors according to Bayes decision rule. Generate some artificial (normally distributed) data, and test your progra ...g the distribution from class 1, then they will classified as class by the bayes classifier unless I choose the distributions of both the classes very close10 KB (1,594 words) - 11:41, 24 March 2008
- =Lecture 17, [[ECE662]]: Decision Theory= [[Lecture 2 - Decision Hypersurfaces_Old Kiwi|2]],6 KB (938 words) - 08:38, 17 January 2013
- =Lecture 1, [[ECE662]]: Decision Theory= [[Lecture 2 - Decision Hypersurfaces_Old Kiwi|2]],3 KB (468 words) - 08:45, 17 January 2013
- =Lecture 2, [[ECE662]]: Decision Theory= [[Lecture 2 - Decision Hypersurfaces_Old Kiwi|2]],5 KB (737 words) - 08:45, 17 January 2013
- =Lecture 3, [[ECE662]]: Decision Theory= [[Lecture 2 - Decision Hypersurfaces_Old Kiwi|2]],5 KB (843 words) - 08:46, 17 January 2013
- =Lecture 5, [[ECE662]]: Decision Theory= [[Lecture 2 - Decision Hypersurfaces_Old Kiwi|2]],6 KB (916 words) - 08:47, 17 January 2013
- =Lecture 6, [[ECE662]]: Decision Theory= [[Lecture 2 - Decision Hypersurfaces_Old Kiwi|2]],9 KB (1,586 words) - 08:47, 17 January 2013
- =Lecture 7, [[ECE662]]: Decision Theory= [[Lecture 2 - Decision Hypersurfaces_Old Kiwi|2]],10 KB (1,488 words) - 10:16, 20 May 2013
- =Lecture 8, [[ECE662]]: Decision Theory= [[Lecture 2 - Decision Hypersurfaces_Old Kiwi|2]],5 KB (792 words) - 08:48, 17 January 2013
- =Lecture 9, [[ECE662]]: Decision Theory= [[Lecture 2 - Decision Hypersurfaces_Old Kiwi|2]],8 KB (1,307 words) - 08:48, 17 January 2013
- =Lecture 10, [[ECE662]]: Decision Theory= [[Lecture 2 - Decision Hypersurfaces_Old Kiwi|2]],5 KB (755 words) - 08:48, 17 January 2013
- =Lecture 11, [[ECE662]]: Decision Theory= [[Lecture 2 - Decision Hypersurfaces_Old Kiwi|2]],5 KB (907 words) - 08:49, 17 January 2013
- =Lecture 12, [[ECE662]]: Decision Theory= [[Lecture 2 - Decision Hypersurfaces_Old Kiwi|2]],8 KB (1,235 words) - 08:49, 17 January 2013
- =Lecture 13, [[ECE662]]: Decision Theory= [[Lecture 2 - Decision Hypersurfaces_Old Kiwi|2]],8 KB (1,354 words) - 08:51, 17 January 2013
- =Lecture 14, [[ECE662]]: Decision Theory= [[Lecture 2 - Decision Hypersurfaces_Old Kiwi|2]],13 KB (2,073 words) - 08:39, 17 January 2013
- =Lecture 15, [[ECE662]]: Decision Theory= [[Lecture 2 - Decision Hypersurfaces_Old Kiwi|2]],7 KB (1,212 words) - 08:38, 17 January 2013
- =Lecture 16, [[ECE662]]: Decision Theory= [[Lecture 2 - Decision Hypersurfaces_Old Kiwi|2]],10 KB (1,607 words) - 08:38, 17 January 2013
- =Lecture 18, [[ECE662]]: Decision Theory= [[Lecture 2 - Decision Hypersurfaces_Old Kiwi|2]],6 KB (1,066 words) - 08:40, 17 January 2013
- This page and its subtopics discusses everything about Bayesian Decision Theory. Lectures discussing Bayesian Decision Theory : [[Lecture 3_Old Kiwi]] and [[Lecture 4_Old Kiwi]]3 KB (558 words) - 17:03, 16 April 2008
- ===A 1967 paper introducing Nearest neighbor algorithm using the Bayes probability of error=== ...bility of error of the nearest neighbor rule is bounded above by twice the Bayes probability of error. In this sense, it may be said that half the classific39 KB (5,715 words) - 10:52, 25 April 2008
- =Lecture 4, [[ECE662]]: Decision Theory= [[Lecture 2 - Decision Hypersurfaces_Old Kiwi|2]],8 KB (1,360 words) - 08:46, 17 January 2013
- [[Category:decision theory]] =Bayes Decision Rule Video=1 KB (172 words) - 11:08, 10 June 2013
- =Lecture 19, [[ECE662]]: Decision Theory= [[Lecture 2 - Decision Hypersurfaces_Old Kiwi|2]],5 KB (1,003 words) - 08:40, 17 January 2013
- =Lecture 20, [[ECE662]]: Decision Theory= [[Lecture 2 - Decision Hypersurfaces_Old Kiwi|2]],6 KB (1,047 words) - 08:42, 17 January 2013
- =Lecture 21, [[ECE662]]: Decision Theory= [[Lecture 2 - Decision Hypersurfaces_Old Kiwi|2]],6 KB (1,012 words) - 08:42, 17 January 2013
- =Lecture 22, [[ECE662]]: Decision Theory= [[Lecture 2 - Decision Hypersurfaces_Old Kiwi|2]],6 KB (806 words) - 08:42, 17 January 2013
- ...PROBABILITY and LIKELIHOOD by forming a POSTERIOR probability using Bayes Rule.2 KB (302 words) - 01:09, 7 April 2008
- ...amples with the a nearest neighbor decision boundary approximate the Bayes decision boundary (Fig. 2). '''Figure 1:''' Two overlapping distributions along with the Bayes decision boundary2 KB (296 words) - 11:48, 7 April 2008
- =Lecture 23, [[ECE662]]: Decision Theory= [[Lecture 2 - Decision Hypersurfaces_Old Kiwi|2]],7 KB (1,060 words) - 08:43, 17 January 2013
- =Lecture 24, [[ECE662]]: Decision Theory= [[Lecture 2 - Decision Hypersurfaces_Old Kiwi|2]],8 KB (1,254 words) - 08:43, 17 January 2013
- =Lecture 25, [[ECE662]]: Decision Theory= [[Lecture 2 - Decision Hypersurfaces_Old Kiwi|2]],8 KB (1,259 words) - 08:43, 17 January 2013
- '''Bayes' classification''' is an ideal classification technique when the true distr * [[Lecture 3 - Bayes classification_Old Kiwi]]2 KB (399 words) - 14:03, 18 June 2008
- =Lecture 26, [[ECE662]]: Decision Theory= [[Lecture 2 - Decision Hypersurfaces_Old Kiwi|2]],8 KB (1,244 words) - 08:44, 17 January 2013
- =Lecture 27, [[ECE662]]: Decision Theory= [[Lecture 2 - Decision Hypersurfaces_Old Kiwi|2]],8 KB (1,337 words) - 08:44, 17 January 2013
- =Lecture 28, [[ECE662]]: Decision Theory= [[Lecture 2 - Decision Hypersurfaces_Old Kiwi|2]],10 KB (1,728 words) - 08:55, 17 January 2013
- [[Category:decision theory]] '''[[ECE662]]: Statistical Pattern Recognition and Decision Making Processes'''5 KB (744 words) - 11:17, 10 June 2013
- ...Spring 2008 edition of the course [[ECE662|ECE662: Pattern Recognition and Decision Making processes]]. * [[Lecture 2 - Decision Hypersurfaces_OldKiwi|Lecture 2 - Decision Hypersurfaces]]7 KB (875 words) - 07:11, 13 February 2012
- [[Category:decision theory]] '''[[ECE662]]: Statistical Pattern Recognition and Decision Making Processes'''3 KB (429 words) - 09:07, 11 January 2016
- <font size="4">'''Upper Bounds for Bayes Error''' <br> </font> <font size="2">A [http://www.projectrhea.org/learning ...s Error. First, in chapter 2, the error bound is expressed in terms of the Bayes classifiers. This error bound expression includes a ''min'' function that b17 KB (2,590 words) - 10:45, 22 January 2015
- [[Category:decision theory]] '''[[ECE662]]: Statistical Pattern Recognition and Decision Making Processes'''9 KB (1,341 words) - 11:15, 10 June 2013
- = [[ECE662]]: "Statistical Pattern Recognition and Decision Making Processes", Spring 2010 = *[[ECE662 topic2 discussions|Is Bayes truly the best?]]4 KB (547 words) - 12:24, 25 June 2010
- ...10|here]]) is a freeform exercise that consists in applying Bayes decision rule to Normally distributed data. The next homework will consists in a peer rev Here is a link to a lab on Bayes Classifier that you might find helpful. Please use it as a reference.4 KB (596 words) - 13:17, 12 November 2010
- | 4. Bayes Rule *The nearest neighbor classification rule.1 KB (165 words) - 08:55, 22 April 2010
- Experiment with making decisions using Bayes rule and parametric density estimation. Summarize your experiments, results, and *Discuss how the error in the density estimate affects the error in the decision.849 B (115 words) - 15:33, 10 May 2010
- ...ntroduced [[Bayes_Decision_Theory|Bayes rule]] for making decisions. (This rule is the basis for this course.) We focused our discussion on the case where649 B (85 words) - 11:41, 13 April 2010
- [[Category:decision theory]] [[Category:Bayes decision rule]]5 KB (694 words) - 12:41, 2 February 2012
- =Bayes Decision Theory= ...uld choose the most likely class given the observation. By following Bayes rule, one achieves the minimum possible probability of error.2 KB (222 words) - 09:25, 15 April 2010
- = What is your favorite decision method?= ...osing the class with the higher prior. [[EE662Sp10OptimalPrediction|Bayes rule is optimal]]. - jvaught6 KB (884 words) - 16:26, 9 May 2010
- ...orem. We then discussed the probability of error when using Bayes decision rule. More precisely, we obtained the Chernoff Bound and the Bhattacharrya bound628 B (86 words) - 09:09, 11 May 2010
- Error bounds for Bayes decision rule: As we know Bayes decision rule guarantees the lowest average error rate; It Does not tell what the probabi5 KB (806 words) - 09:08, 11 May 2010
- '''Use in Decision Theory''' *[[Homework_1_OldKiwi|Experimenting with Bayes rule]] (from [[ECE662]])2 KB (286 words) - 05:45, 29 December 2010
- =Glossary for "Decision Theory" ([[ECE662]])= == [[Bayes Decision Rule_Old Kiwi|Bayes Decision Rule]] ==31 KB (4,787 words) - 18:21, 22 October 2010
- * [[Bayesian Decision Theory for Normally Distributed Features]] * [[Decision Trees]]1 KB (164 words) - 06:47, 18 November 2010
- *[[ECE662:Glossary_Old_Kiwi|Decision Theory Glossary]] *[[Bayes_Rate_Fallacy:_Bayes_Rules_under_Severe_Class_Imbalance|Bayes rule under severe class imbalance]]1 KB (156 words) - 12:26, 27 March 2015
- ...imality_bayes_decision_rule_michaux_ECE662S14|Optimality of Bayes Decision Rule]], by Aaron Michaux1 KB (140 words) - 12:14, 27 March 2015
- ...decision theory today, namely Bayes decision rule. We first presented the rule for discrete-valued feature vectors, and illustrated it using the previousl ...student's notes for Lecture 3 from ECE662 Spring 2008]] (introducing Bayes Rule)2 KB (259 words) - 12:30, 23 February 2012
- Today we began talking about an important subject in decision theory: Bayes rule for normally distributed feature vectors. We proposed a simple discriminant2 KB (298 words) - 12:31, 23 February 2012
- [[Category:decision theory]] [[Category:Bayes rule]]918 B (134 words) - 13:18, 8 March 2012
- [[Category:decision theory]] [[Category:Bayes rule]]2 KB (320 words) - 12:21, 12 February 2012
- [[Category:decision theory]] '''[[ECE662]]: Statistical Pattern Recognition and Decision Making Processes'''3 KB (413 words) - 11:17, 10 June 2013
- [[Category:decision theory]] '''[[ECE662]]: Statistical Pattern Recognition and Decision Making Processes'''6 KB (874 words) - 11:17, 10 June 2013
- [[Category:decision theory]] '''[[ECE662]]: Statistical Pattern Recognition and Decision Making Processes'''8 KB (1,403 words) - 11:17, 10 June 2013
- '''[[ECE662]]: Statistical Pattern Recognition and Decision Making Processes''' [[Lecture 2 - Decision Hypersurfaces_OldKiwi|2]]|10 KB (1,609 words) - 11:22, 10 June 2013
- '''[[ECE662]]: Statistical Pattern Recognition and Decision Making Processes''' [[Lecture 2 - Decision Hypersurfaces_OldKiwi|2]]|6 KB (977 words) - 11:22, 10 June 2013
- '''[[ECE662]]: Statistical Pattern Recognition and Decision Making Processes''' [[Lecture 2 - Decision Hypersurfaces_OldKiwi|2]]|7 KB (1,098 words) - 11:22, 10 June 2013
- [[Category:decision theory]] '''[[ECE662]]: Statistical Pattern Recognition and Decision Making Processes'''10 KB (1,604 words) - 11:17, 10 June 2013
- [[Category:decision theory]] '''[[ECE662]]: Statistical Pattern Recognition and Decision Making Processes'''10 KB (1,472 words) - 11:16, 10 June 2013
- [[Category:decision theory]] '''[[ECE662]]: Statistical Pattern Recognition and Decision Making Processes'''6 KB (946 words) - 11:17, 10 June 2013
- [[Category:decision theory]] '''[[ECE662]]: Statistical Pattern Recognition and Decision Making Processes'''6 KB (833 words) - 11:16, 10 June 2013
- '''[[ECE662]]: Statistical Pattern Recognition and Decision Making Processes''' [[Lecture 2 - Decision Hypersurfaces_OldKiwi|2]]|6 KB (813 words) - 11:18, 10 June 2013
- '''[[ECE662]]: Statistical Pattern Recognition and Decision Making Processes''' [[Lecture 2 - Decision Hypersurfaces_OldKiwi|2]]|6 KB (946 words) - 11:18, 10 June 2013
- '''[[ECE662]]: Statistical Pattern Recognition and Decision Making Processes''' [[Lecture 2 - Decision Hypersurfaces_OldKiwi|2]]|8 KB (1,278 words) - 11:19, 10 June 2013
- '''[[ECE662]]: Statistical Pattern Recognition and Decision Making Processes''' [[Lecture 2 - Decision Hypersurfaces_OldKiwi|2]]|9 KB (1,389 words) - 11:19, 10 June 2013
- '''[[ECE662]]: Statistical Pattern Recognition and Decision Making Processes''' [[Lecture 2 - Decision Hypersurfaces_OldKiwi|2]]|13 KB (2,098 words) - 11:21, 10 June 2013
- '''[[ECE662]]: Statistical Pattern Recognition and Decision Making Processes''' [[Lecture 2 - Decision Hypersurfaces_OldKiwi|2]]|8 KB (1,246 words) - 11:21, 10 June 2013
- '''[[ECE662]]: Statistical Pattern Recognition and Decision Making Processes''' [[Lecture 2 - Decision Hypersurfaces_OldKiwi|2]]|6 KB (1,041 words) - 11:22, 10 June 2013
- '''[[ECE662]]: Statistical Pattern Recognition and Decision Making Processes''' [[Lecture 2 - Decision Hypersurfaces_OldKiwi|2]]|7 KB (1,082 words) - 11:23, 10 June 2013
- '''[[ECE662]]: Statistical Pattern Recognition and Decision Making Processes''' [[Lecture 2 - Decision Hypersurfaces_OldKiwi|2]]|7 KB (1,055 words) - 11:23, 10 June 2013
- '''[[ECE662]]: Statistical Pattern Recognition and Decision Making Processes''' [[Lecture 2 - Decision Hypersurfaces_OldKiwi|2]]|6 KB (837 words) - 11:23, 10 June 2013
- '''[[ECE662]]: Statistical Pattern Recognition and Decision Making Processes''' [[Lecture 2 - Decision Hypersurfaces_OldKiwi|2]]|7 KB (1,091 words) - 11:23, 10 June 2013
- '''[[ECE662]]: Statistical Pattern Recognition and Decision Making Processes''' [[Lecture 2 - Decision Hypersurfaces_OldKiwi|2]]|9 KB (1,276 words) - 11:24, 10 June 2013
- '''[[ECE662]]: Statistical Pattern Recognition and Decision Making Processes''' [[Lecture 2 - Decision Hypersurfaces_OldKiwi|2]]|8 KB (1,299 words) - 11:24, 10 June 2013
- [[Category:decision theory]] [[Category:Bayes rule]]1 KB (164 words) - 14:25, 30 May 2012
- '''[[ECE662]]: Statistical Pattern Recognition and Decision Making Processes''' [[Lecture 2 - Decision Hypersurfaces_OldKiwi|2]]|8 KB (1,214 words) - 11:24, 10 June 2013
- [[Category:decision theory]] '''[[ECE662]]: Statistical Pattern Recognition and Decision Making Processes'''8 KB (1,313 words) - 11:24, 10 June 2013
- [[Category:decision theory]] '''[[ECE662]]: Statistical Pattern Recognition and Decision Making Processes'''10 KB (1,704 words) - 11:25, 10 June 2013
- = Bayes Decision Theory - Introduction = The Bayesian decision theory is a valuable approach to solve a pattern classification problem. It5 KB (844 words) - 23:32, 28 February 2013
- :↳ [[Bayes_theorem_S13|Bayes' Theorem]] <pre>keyword: probability, Monty Hall, Bayes' Theorem, Bayes' Rule </pre>5 KB (925 words) - 13:09, 25 November 2013
- == Illustration of Bayes Rule == ...ay, we will be looking at a real world illustration where we can use Bayes Rule to solve a problem.3 KB (415 words) - 18:34, 22 March 2013
- [[Category:decision theory]] '''[[ECE662]]: Statistical Pattern Recognition and Decision Making Processes'''3 KB (425 words) - 09:59, 4 November 2013
- == [[ECE662]]: '''Statistical Pattern Recognition and Decision Making Processes, Spring 2014''' (cross-listed with CS662) == *Slectures on Bayes Rule10 KB (1,450 words) - 20:50, 2 May 2016
- [[Category:Bayes' Theorem]] [[Category:Bayes' Rule]]14 KB (2,241 words) - 10:42, 22 January 2015
- '''Upper Bounds for Bayes Error''' <br /> ...hen we will present the probability of error that results from using Bayes rule.13 KB (2,062 words) - 10:45, 22 January 2015
- Classification using Bayes Rule in 1-dimensional and N-dimensional feature spaces ...nal feature space. So, we will take a look at what the definition of Bayes rule is, how it can be used for the classification task with examples, and how w19 KB (3,255 words) - 10:47, 22 January 2015
- *A working example using PCA with Bayes rule in classification #Use PCA with Bayes rule in classification22 KB (3,459 words) - 10:40, 22 January 2015
- <font size="4">'''Neyman-Pearson: How Bayes Decision Rule Controls Error''' <br> </font> <font size="2">A [https://www.projectrhea.or7 KB (509 words) - 19:30, 2 May 2014
- '''Bayes Rule for Minimizing Risk''' <br /> In class we discussed Bayes rule for minimizing the probability of error.12 KB (1,810 words) - 10:46, 22 January 2015
- <font size="4">'''Bayes rule in practice''' <br> </font> <font size="2">A [http://www.projectrhea.org/le ...ng data with unknown parameters, and testing data is classified with Bayes rule.<br>7 KB (1,177 words) - 10:47, 22 January 2015