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  • ECE662 topic2 discussions
    =Is Bayes' Rule Truly the Best?=
    535 B (72 words) - 10:09, 1 March 2010
  • EE662Sp10BayesExample
    Or, equivalently, we can use Bayes' Rule explicity. Bayes' Rule is:
    7 KB (948 words) - 04:35, 2 February 2010
  • Hw1 ECE662Spring2010
    :Experiment with Bayes rule for normally distributed features. Summarize your experiments, results, and
    1 KB (149 words) - 09:07, 6 October 2010
  • ECE662 hw1 discussions
    ...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
    4 KB (596 words) - 13:17, 12 November 2010
  • Star feedbackECE662S2010
    ...Bayes_Rate_Fallacy:_Bayes_Rules_under_Severe_Class_Imbalance|page on Bayes rule under severe class imbalance]] ...esome [[EE662Sp10OptimalPrediction|page discussing the optimality of Bayes rule]].
    7 KB (1,009 words) - 11:27, 13 April 2010
  • OutlineECE662S10
    | 4. Bayes Rule *The nearest neighbor classification rule.
    1 KB (165 words) - 08:55, 22 April 2010
  • Hw2 ECE662Spring2010
    Experiment with making decisions using Bayes rule and parametric density estimation. Summarize your experiments, results, and
    849 B (115 words) - 15:33, 10 May 2010
  • Hw3 ECE662Spring2010
    Experiment with making decisions using Bayes rule and non-parametric density estimation. Summarize your experiments, results,
    904 B (122 words) - 15:16, 10 May 2010
  • Lecture4ECE662S10
    ...ntroduced [[Bayes_Decision_Theory|Bayes rule]] for making decisions. (This rule is the basis for this course.) We focused our discussion on the case where
    649 B (85 words) - 11:41, 13 April 2010
  • Lecture5ECE662S10
    ...that the example previously proposed performs worse]] than following Bayes rule. ...expected loss (called "risk") when following [[Bayes_Decision_Theory|Bayes rule]].
    968 B (131 words) - 11:42, 13 April 2010
  • Lecture6ECE662S10
    ...minant functions]] and their relationship to [[Bayes_Decision_Theory|Bayes rule]]. We focused on discriminant functions when the class densities are normal
    462 B (56 words) - 08:48, 11 May 2010
  • Bayes Rate Fallacy: Bayes Rules under Severe Class Imbalance
    [[Category:Bayes decision rule]] [[Bayes Decision Theory|Bayes decision rule]] is a simple, intuitive and powerful classifier. It allows to select the m
    5 KB (694 words) - 12:41, 2 February 2012
  • Lecture23ECE662S10
    ...ut [[Bayes Rate Fallacy: Bayes Rules under Severe Class Imbalance‎|Bayes rule under severe class imbalance]]. Please join in!
    1 KB (210 words) - 09:20, 15 April 2010
  • Bayes Decision Theory
    ...uld choose the most likely class given the observation. By following Bayes rule, one achieves the minimum possible probability of error. ...ture_3_-_Bayes_classification_OldKiwi|Lecture 3 introducing Bayes decision rule]]
    2 KB (222 words) - 09:25, 15 April 2010
  • FavoritedecisionECE662S10
    ...osing the class with the higher prior. [[EE662Sp10OptimalPrediction|Bayes rule is optimal]]. - jvaught
    6 KB (884 words) - 16:26, 9 May 2010
  • Lecture8ECE662S10
    ...orem. We then discussed the probability of error when using Bayes decision rule. More precisely, we obtained the Chernoff Bound and the Bhattacharrya bound
    628 B (86 words) - 09:09, 11 May 2010
  • Noteslecture8ECE662S10
    Error bounds for Bayes decision rule: As we know Bayes decision rule guarantees the lowest average error rate; It Does not tell what the probabi
    5 KB (806 words) - 09:08, 11 May 2010
  • Presentation Use of MATLAB in classroom
    *[[Homework_1_OldKiwi|Experimenting with Bayes rule]] (from [[ECE662]])
    2 KB (286 words) - 05:45, 29 December 2010
  • Decision Theory Glossary ECE662Spring2008
    == [[Bayes Decision Rule_Old Kiwi|Bayes Decision Rule]] == Bayes' decision rule creates an objective function which minimizes the probability of error (mis
    31 KB (4,787 words) - 18:21, 22 October 2010
  • SA1050
    *[[Bayes_Rate_Fallacy:_Bayes_Rules_under_Severe_Class_Imbalance|Bayes rule under severe class imbalance]]
    1 KB (164 words) - 06:47, 18 November 2010
  • Machine learning tutorials
    *[[Bayes_Rate_Fallacy:_Bayes_Rules_under_Severe_Class_Imbalance|Bayes rule under severe class imbalance]]
    1 KB (156 words) - 12:26, 27 March 2015
  • Machine learning slectures
    ...imality_bayes_decision_rule_michaux_ECE662S14|Optimality of Bayes Decision Rule]], by Aaron Michaux
    1 KB (140 words) - 12:14, 27 March 2015
  • ECE 600 Prerequisites Bayes' Theorem
    • = Bayes' rule
    717 B (138 words) - 11:23, 30 November 2010
  • Lecture4ECE662S12
    ...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
  • Lecture5ECE662S12
    ...covered the discriminant functions that could be used to implement such a rule.
    1 KB (187 words) - 12:30, 23 February 2012
  • Lecture6ECE662S12
    Today we began talking about an important subject in decision theory: Bayes rule for normally distributed feature vectors. We proposed a simple discriminant
    2 KB (298 words) - 12:31, 23 February 2012
  • Hw1 ECE662 S12
    [[Category:Bayes rule]] Experiment with Bayes rule for normally distributed features. Summarize your experiments, results, and
    918 B (134 words) - 13:18, 8 March 2012
  • Hw1 discussion ECE662 S12
    [[Category:Bayes rule]]
    2 KB (320 words) - 12:21, 12 February 2012
  • Lecture 1 - Introduction OldKiwi
    [[Lecture 17 - Nearest Neighbors Clarification Rule and Metrics_OldKiwi|17]]| [[Lecture 18 - Nearest Neighbors Clarification Rule and Metrics(Continued)_OldKiwi|18]]|
    3 KB (413 words) - 11:17, 10 June 2013
  • Lecture 3 - Bayes classification OldKiwi
    [[Lecture 17 - Nearest Neighbors Clarification Rule and Metrics_OldKiwi|17]]| [[Lecture 18 - Nearest Neighbors Clarification Rule and Metrics(Continued)_OldKiwi|18]]|
    6 KB (874 words) - 11:17, 10 June 2013
  • Lecture 4 - Bayes Classification OldKiwi
    [[Lecture 17 - Nearest Neighbors Clarification Rule and Metrics_OldKiwi|17]]| [[Lecture 18 - Nearest Neighbors Clarification Rule and Metrics(Continued)_OldKiwi|18]]|
    8 KB (1,403 words) - 11:17, 10 June 2013
  • Lecture 16 - Parzen Window Method and K-nearest Neighbor Density Estimate OldKiwi
    [[Lecture 17 - Nearest Neighbors Clarification Rule and Metrics_OldKiwi|17]]| [[Lecture 18 - Nearest Neighbors Clarification Rule and Metrics(Continued)_OldKiwi|18]]|
    10 KB (1,609 words) - 11:22, 10 June 2013
  • Lecture 17 - Nearest Neighbors Clarification Rule and Metrics OldKiwi
    [[Lecture 17 - Nearest Neighbors Clarification Rule and Metrics_OldKiwi|17]]| [[Lecture 18 - Nearest Neighbors Clarification Rule and Metrics(Continued)_OldKiwi|18]]|
    6 KB (977 words) - 11:22, 10 June 2013
  • Lecture 18 - Nearest Neighbors Clarification Rule and Metrics(Continued) OldKiwi
    [[Lecture 17 - Nearest Neighbors Clarification Rule and Metrics_OldKiwi|17]]| [[Lecture 18 - Nearest Neighbors Clarification Rule and Metrics(Continued)_OldKiwi|18]]|
    7 KB (1,098 words) - 11:22, 10 June 2013
  • Lecture 6 - Discriminant Functions OldKiwi
    [[Lecture 17 - Nearest Neighbors Clarification Rule and Metrics_OldKiwi|17]]| [[Lecture 18 - Nearest Neighbors Clarification Rule and Metrics(Continued)_OldKiwi|18]]|
    10 KB (1,604 words) - 11:17, 10 June 2013
  • Lecture 7 - MLE and BPE OldKiwi
    [[Lecture 17 - Nearest Neighbors Clarification Rule and Metrics_OldKiwi|17]]| [[Lecture 18 - Nearest Neighbors Clarification Rule and Metrics(Continued)_OldKiwi|18]]|
    10 KB (1,472 words) - 11:16, 10 June 2013
  • Lecture 5 - Discriminant Functions OldKiwi
    [[Lecture 17 - Nearest Neighbors Clarification Rule and Metrics_OldKiwi|17]]| [[Lecture 18 - Nearest Neighbors Clarification Rule and Metrics(Continued)_OldKiwi|18]]|
    6 KB (946 words) - 11:17, 10 June 2013
  • Lecture 8 - MLE, BPE and Linear Discriminant Functions OldKiwi
    [[Lecture 17 - Nearest Neighbors Clarification Rule and Metrics_OldKiwi|17]]| [[Lecture 18 - Nearest Neighbors Clarification Rule and Metrics(Continued)_OldKiwi|18]]|
    6 KB (833 words) - 11:16, 10 June 2013
  • Lecture 10 - Batch Perceptron and Fisher Linear Discriminant OldKiwi
    [[Lecture 17 - Nearest Neighbors Clarification Rule and Metrics_OldKiwi|17]]| [[Lecture 18 - Nearest Neighbors Clarification Rule and Metrics(Continued)_OldKiwi|18]]|
    6 KB (813 words) - 11:18, 10 June 2013
  • Lecture 11 - Fischer's Linear Discriminant again OldKiwi
    [[Lecture 17 - Nearest Neighbors Clarification Rule and Metrics_OldKiwi|17]]| [[Lecture 18 - Nearest Neighbors Clarification Rule and Metrics(Continued)_OldKiwi|18]]|
    6 KB (946 words) - 11:18, 10 June 2013
  • Lecture 12 - Support Vector Machine and Quadratic Optimization Problem OldKiwi
    [[Lecture 17 - Nearest Neighbors Clarification Rule and Metrics_OldKiwi|17]]| [[Lecture 18 - Nearest Neighbors Clarification Rule and Metrics(Continued)_OldKiwi|18]]|
    8 KB (1,278 words) - 11:19, 10 June 2013
  • Lecture 13 - Kernel function for SVMs and ANNs introduction OldKiwi
    [[Lecture 17 - Nearest Neighbors Clarification Rule and Metrics_OldKiwi|17]]| [[Lecture 18 - Nearest Neighbors Clarification Rule and Metrics(Continued)_OldKiwi|18]]|
    9 KB (1,389 words) - 11:19, 10 June 2013
  • Lecture 14 - ANNs, Non-parametric Density Estimation (Parzen Window) OldKiwi
    [[Lecture 17 - Nearest Neighbors Clarification Rule and Metrics_OldKiwi|17]]| [[Lecture 18 - Nearest Neighbors Clarification Rule and Metrics(Continued)_OldKiwi|18]]|
    13 KB (2,098 words) - 11:21, 10 June 2013
  • Lecture 15 - Parzen Window Method OldKiwi
    [[Lecture 17 - Nearest Neighbors Clarification Rule and Metrics_OldKiwi|17]]| [[Lecture 18 - Nearest Neighbors Clarification Rule and Metrics(Continued)_OldKiwi|18]]|
    8 KB (1,246 words) - 11:21, 10 June 2013
  • Lecture 19 - Nearest Neighbor Error Rates OldKiwi
    [[Lecture 17 - Nearest Neighbors Clarification Rule and Metrics_OldKiwi|17]]| [[Lecture 18 - Nearest Neighbors Clarification Rule and Metrics(Continued)_OldKiwi|18]]|
    6 KB (1,041 words) - 11:22, 10 June 2013
  • Lecture 20 - Density Estimation using Series Expansion and Decision Trees OldKiwi
    [[Lecture 17 - Nearest Neighbors Clarification Rule and Metrics_OldKiwi|17]]| [[Lecture 18 - Nearest Neighbors Clarification Rule and Metrics(Continued)_OldKiwi|18]]|
    7 KB (1,082 words) - 11:23, 10 June 2013
  • Lecture 21 - Decision Trees(Continued) OldKiwi
    [[Lecture 17 - Nearest Neighbors Clarification Rule and Metrics_OldKiwi|17]]| [[Lecture 18 - Nearest Neighbors Clarification Rule and Metrics(Continued)_OldKiwi|18]]|
    7 KB (1,055 words) - 11:23, 10 June 2013
  • Lecture 22 - Decision Trees and Clustering OldKiwi
    [[Lecture 17 - Nearest Neighbors Clarification Rule and Metrics_OldKiwi|17]]| [[Lecture 18 - Nearest Neighbors Clarification Rule and Metrics(Continued)_OldKiwi|18]]|
    6 KB (837 words) - 11:23, 10 June 2013
  • Lecture 23 - Spanning Trees OldKiwi
    [[Lecture 17 - Nearest Neighbors Clarification Rule and Metrics_OldKiwi|17]]| [[Lecture 18 - Nearest Neighbors Clarification Rule and Metrics(Continued)_OldKiwi|18]]|
    7 KB (1,091 words) - 11:23, 10 June 2013
  • Lecture 24 - Clustering and Hierarchical Clustering OldKiwi
    [[Lecture 17 - Nearest Neighbors Clarification Rule and Metrics_OldKiwi|17]]| [[Lecture 18 - Nearest Neighbors Clarification Rule and Metrics(Continued)_OldKiwi|18]]|
    9 KB (1,276 words) - 11:24, 10 June 2013

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