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  • 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
  • Lecture 25 - Clustering Algorithms OldKiwi
    [[Lecture 17 - Nearest Neighbors Clarification Rule and Metrics_OldKiwi|17]]| [[Lecture 18 - Nearest Neighbors Clarification Rule and Metrics(Continued)_OldKiwi|18]]|
    8 KB (1,299 words) - 11:24, 10 June 2013
  • Hw2 ECE662 S12
    [[Category:Bayes rule]]
    1 KB (164 words) - 14:25, 30 May 2012
  • Lecture 26 - Statistical Clustering Methods OldKiwi
    [[Lecture 17 - Nearest Neighbors Clarification Rule and Metrics_OldKiwi|17]]| [[Lecture 18 - Nearest Neighbors Clarification Rule and Metrics(Continued)_OldKiwi|18]]|
    8 KB (1,214 words) - 11:24, 10 June 2013
  • Lecture 27 - Clustering by finding valleys of densities OldKiwi
    [[Lecture 17 - Nearest Neighbors Clarification Rule and Metrics_OldKiwi|17]]| [[Lecture 18 - Nearest Neighbors Clarification Rule and Metrics(Continued)_OldKiwi|18]]|
    8 KB (1,313 words) - 11:24, 10 June 2013
  • Lecture 28 - Final lecture OldKiwi
    [[Lecture 17 - Nearest Neighbors Clarification Rule and Metrics_OldKiwi|17]]| [[Lecture 18 - Nearest Neighbors Clarification Rule and Metrics(Continued)_OldKiwi|18]]|
    10 KB (1,704 words) - 11:25, 10 June 2013
  • ECE-QE CS1-2011 solusion-1
    \color{green}\text{It should be added: According to the Bayes rule:}
    8 KB (1,247 words) - 10:29, 13 September 2013
  • Lecture6 blog ECE302S13 Boutin
    In Lecture 6, we presented the total probability theorem and Bayes rule. We illustrated both of these using a chess tournament example. We also ill
    3 KB (363 words) - 06:30, 23 January 2013
  • Green26 ece302 ec1
    <math style='inline'>= \frac{P(im_3)P(R|im_3)}{P(R)}</math> from Bayes' rule
    5 KB (779 words) - 19:36, 27 January 2013
  • Bayesian Decision Theory
    ...e we only have two variables). We can therefore use the following decision rule; that if ''P(x<sub>1</sub>)'' > ''P(x<sub>2</sub>)'', then the card is diam
    5 KB (844 words) - 23:32, 28 February 2013
  • How to get started math squad Spring2013
    **Probability: Computing the probability of false alarm using Bayes rule. Give examples related to diseases testing, pregnancy tests, radar detectio
    3 KB (555 words) - 17:17, 18 March 2013
  • Bayes theorem S13
    [[Category:bayes rule]] <pre>keyword: probability, Bayes' Theorem, Bayes' Rule </pre>
    4 KB (649 words) - 13:08, 25 November 2013
  • Bayes theorem eg1 S13
    <pre>keyword: probability, Bayes' Theorem, Bayes' Rule </pre>
    4 KB (592 words) - 13:09, 25 November 2013
  • Bayes theorem eg2 S13
    <pre>keyword: probability, false positive, Bayes' Theorem, Bayes' Rule </pre>
    3 KB (562 words) - 13:09, 25 November 2013
  • Bayes theorem eg3 S13
    <pre>keyword: probability, Monty Hall, Bayes' Theorem, Bayes' Rule </pre>
    5 KB (925 words) - 13:09, 25 November 2013
  • Illustration of The Bayesian Decision Theory
    == 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
  • ECE662 Pattern Recognition Decision Making Processes Spring2008 sLecture collective
    ...tion Rule and Metrics_OldKiwi|Lecture 17 - Nearest Neighbors Clarification Rule and Metrics]] ...nd Metrics(Continued)_OldKiwi|Lecture 18 - Nearest Neighbors Clarification Rule and Metrics(Continued)]]
    3 KB (425 words) - 09:59, 4 November 2013
  • 2014 Spring ECE 662 Boutin
    *Slectures on Bayes Rule **Bayes Rule in Layman's Terms
    10 KB (1,450 words) - 20:50, 2 May 2016
  • From Bayes Theorem to Pattern Recognition via Bayes Rule
    [[Category:Bayes' Rule]] '''From Bayes' Theorem to Pattern Recognition via Bayes' Rule''' <br />
    14 KB (2,241 words) - 10:42, 22 January 2015
  • Lecture 3 - Bayes Theorem and its Application to Pattern Recognition OldKiwi
    #REDIRECT [[From Bayes Theorem to Pattern Recognition via Bayes Rule]]
    70 B (10 words) - 07:10, 12 February 2014
  • Upper Bounds for Bayes Error
    ...hen we will present the probability of error that results from using Bayes rule. When Bayes rule is used the resulting probability of error is the smallest possible error,
    13 KB (2,062 words) - 10:45, 22 January 2015

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