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Page title matches

  • Bayes Decision Rule Old Kiwi
    =Bayes Decision Rule Video= The video demonstrates Bayes decision rule on 2D feature data from two classes. We visualize the decision hyper surfac
    1 KB (172 words) - 11:08, 10 June 2013
  • 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
  • Bayes Rule for 1-dimensional and N-dimensional feature spaces
    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 w
    19 KB (3,255 words) - 10:47, 22 January 2015
  • Slecture Bayes rule to minimize risk Andy Park ECE662 Spring 2014
    Bayes Rule to Minimize Risk ==Part 1: Introduction - Revisit Bayes Rule/Classifier ==
    2 KB (226 words) - 10:45, 22 January 2015
  • Bayes Rule Minimize Risk Dennis Lee
    '''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
  • Comments Bayes Rule Dennis Lee
    Comments for [[Bayes_Rule_Minimize_Risk_Dennis_Lee| Bayes Rule for Minimizing Risk]] ...t function which is the Expected Risk, and finally states a classification rule that would minimize it. Then, he gives two clear examples for 1D and 2D fea
    3 KB (504 words) - 16:04, 30 April 2014
  • Derivation of Bayes Rule from Bayes Theorem
    [[Category:Bayes Rule]] '''Derivation of Bayes' Rule from Bayes' Theorem ''' <br />
    628 B (83 words) - 18:52, 20 April 2014
  • Derivation of Bayes' Rule from Bayes' Theorem
    [[Category:Bayes Rule]] '''Derivation of Bayes' Rule from Bayes' Theorem ''' <br />
    927 B (122 words) - 10:42, 22 January 2015
  • Bayes rule in practice
    <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
  • Slecture Bayes rule to minimize risk Andy Park review
    ...[[Slecture_Bayes_rule_to_minimize_risk_Andy_Park_ECE662_Spring_2014| Bayes Rule to Minimize Risk]]''' </font> ...erical deviration. Finally, likelihood ratio test is associated with Bayes rule.
    2 KB (303 words) - 09:59, 12 May 2014
  • Bayes rule in practice review
    <font size="4">Questions and Comments for: '''[[Bayes rule in practice|Bayes rule in practice]]''' </font> ...the estimated parameters were used to classify the testing data with Bayes rule.
    2 KB (259 words) - 12:40, 2 May 2014
  • Derivation Bayes Rule slecture ECE662 Spring2014 Kim
    [[Category:Bayes Rule]] '''Derivation of Bayes' Rule ''' <br />
    924 B (123 words) - 10:43, 22 January 2015
  • Derivation of Bayes rule In Greek
    [[Category:Bayes' Rule]] '''Derivation of Bayes rule (In Greek)''' <br />
    18 KB (665 words) - 10:43, 22 January 2015
  • Bayes rule
    =Bayes rule=
    1 KB (171 words) - 06:18, 29 April 2014
  • Derivation of Bayes rule In Chinese
    <font size="4">Bayes Rule and Its Applications </font>
    6 KB (535 words) - 10:43, 22 January 2015
  • Derivation of Bayes rule Anonymous7
    Derivation of Bayes Rule * Bayes rule statement.
    7 KB (1,106 words) - 10:42, 22 January 2015
  • Slecture optimality bayes decision rule michaux ECE662S14
    == Proof of the Optimality of Bayes' Decision Rule ==
    774 B (101 words) - 10:43, 22 January 2015
  • Classification using Bayes Rule in 1-dimensional and N-dimensional feature spaces Review
    ..._1-dimensional_and_N-dimensional_feature_spaces|Classification using Bayes Rule in 1-dimensional and N-dimensional feature spaces]] ...at use Bayes theorem. The author then discussed classification using Byes rule and derived the error formula for calculating the error when classifying 1
    2 KB (359 words) - 09:58, 3 May 2014
  • Derivation of Bayes rule In Chinese review
    ...excellent. It definitely gives a good and fairly complete review of Bayes' rule. It is also very well organized, first the definition (What is Bayes' thero LZ Comment1: I like your example for Bayes' rule. It is a simple, typical and real-world problem of solving the posterior pr
    1 KB (223 words) - 19:55, 3 May 2014
  • Introduction to Bayes' Rule
    [[Category:Bayes Rule]] '''Introduction to Bayes' Rule in Layman's Terms''' <br />
    892 B (116 words) - 10:42, 22 January 2015

Page text matches

  • HW2.5 Landis Huffman ECE302Fall2008sanghavi
    Hint: Recall Bayes' Rule:
    111 B (26 words) - 06:40, 4 September 2008
  • HW3.4c AJ Hartnett ECE302Fall2008sanghavi
    Following Bayes rule we can get:
    620 B (135 words) - 06:56, 16 September 2008
  • Cheat1 ECE302Fall2008sanghavi
    '''Bayes rule and total probability'''
    3 KB (525 words) - 13:04, 22 November 2011
  • Cheat2 ECE302Fall2008sanghavi
    == Continuous Bayes' rule: ==
    4 KB (722 words) - 13:05, 22 November 2011
  • Map Estimation ECE302Fall2008sanghavi
    Using Bayes' Rule, we can expand the posterior <math>f_{\theta | X}(\theta | X)</math>:
    4 KB (671 words) - 09:23, 10 May 2013
  • ECE662:BoutinSpring08 Old Kiwi
    * [[Lecture 17 - Nearest Neighbors Clarification Rule and Metrics_Old Kiwi]] * [[Lecture 18 - Nearest Neighbors Clarification Rule and Metrics(Continued)_Old Kiwi]]
    6 KB (747 words) - 05:18, 5 April 2013
  • ECE662:Glossary Old Kiwi
    == [[Bayes Decision Rule_Old Kiwi|Bayes Decision Rule]] == Bayes' decision rule creates an objective function which minimizes the probability of error (mis
    31 KB (4,832 words) - 18:13, 22 October 2010
  • ECE662:Homework 1 Old Kiwi
    ...er program that classifies the feature vectors according to Bayes decision rule. Generate some artificial (normally distributed) data, and test your progra
    10 KB (1,594 words) - 11:41, 24 March 2008
  • Lecture 17 - Nearest Neighbors Clarification Rule and Metrics Old Kiwi
    [[Lecture 17 - Nearest Neighbors Clarification Rule and Metrics_Old Kiwi|17]], [[Lecture 18 - Nearest Neighbors Clarification Rule and Metrics(Continued)_Old Kiwi|18]],
    6 KB (938 words) - 08:38, 17 January 2013
  • Lecture 1 - Introduction Old Kiwi
    [[Lecture 17 - Nearest Neighbors Clarification Rule and Metrics_Old Kiwi|17]], [[Lecture 18 - Nearest Neighbors Clarification Rule and Metrics(Continued)_Old Kiwi|18]],
    3 KB (468 words) - 08:45, 17 January 2013
  • Lecture 2 - Decision Hypersurfaces Old Kiwi
    [[Lecture 17 - Nearest Neighbors Clarification Rule and Metrics_Old Kiwi|17]], [[Lecture 18 - Nearest Neighbors Clarification Rule and Metrics(Continued)_Old Kiwi|18]],
    5 KB (737 words) - 08:45, 17 January 2013
  • Lecture 3 - Bayes classification Old Kiwi
    [[Lecture 17 - Nearest Neighbors Clarification Rule and Metrics_Old Kiwi|17]], [[Lecture 18 - Nearest Neighbors Clarification Rule and Metrics(Continued)_Old Kiwi|18]],
    5 KB (843 words) - 08:46, 17 January 2013
  • Lecture 5 - Discriminant Functions Old Kiwi
    [[Lecture 17 - Nearest Neighbors Clarification Rule and Metrics_Old Kiwi|17]], [[Lecture 18 - Nearest Neighbors Clarification Rule and Metrics(Continued)_Old Kiwi|18]],
    6 KB (916 words) - 08:47, 17 January 2013
  • Lecture 6 - Discriminant Functions Old Kiwi
    [[Lecture 17 - Nearest Neighbors Clarification Rule and Metrics_Old Kiwi|17]], [[Lecture 18 - Nearest Neighbors Clarification Rule and Metrics(Continued)_Old Kiwi|18]],
    9 KB (1,586 words) - 08:47, 17 January 2013
  • Lecture 7 - MLE and BPE Old Kiwi
    [[Lecture 17 - Nearest Neighbors Clarification Rule and Metrics_Old Kiwi|17]], [[Lecture 18 - Nearest Neighbors Clarification Rule and Metrics(Continued)_Old Kiwi|18]],
    10 KB (1,488 words) - 10:16, 20 May 2013
  • Lecture 8 - MLE, BPE and Linear Discriminant Functions Old Kiwi
    [[Lecture 17 - Nearest Neighbors Clarification Rule and Metrics_Old Kiwi|17]], [[Lecture 18 - Nearest Neighbors Clarification Rule and Metrics(Continued)_Old Kiwi|18]],
    5 KB (792 words) - 08:48, 17 January 2013
  • Lecture 9 - Linear Discriminant Functions Old Kiwi
    [[Lecture 17 - Nearest Neighbors Clarification Rule and Metrics_Old Kiwi|17]], [[Lecture 18 - Nearest Neighbors Clarification Rule and Metrics(Continued)_Old Kiwi|18]],
    8 KB (1,307 words) - 08:48, 17 January 2013
  • Lecture 10 - Batch Perceptron and Fisher Linear Discriminant Old Kiwi
    [[Lecture 17 - Nearest Neighbors Clarification Rule and Metrics_Old Kiwi|17]], [[Lecture 18 - Nearest Neighbors Clarification Rule and Metrics(Continued)_Old Kiwi|18]],
    5 KB (755 words) - 08:48, 17 January 2013
  • Lecture 11 - Fischer's Linear Discriminant again Old Kiwi
    [[Lecture 17 - Nearest Neighbors Clarification Rule and Metrics_Old Kiwi|17]], [[Lecture 18 - Nearest Neighbors Clarification Rule and Metrics(Continued)_Old Kiwi|18]],
    5 KB (907 words) - 08:49, 17 January 2013
  • Lecture 12 - Support Vector Machine and Quadratic Optimization Problem Old Kiwi
    [[Lecture 17 - Nearest Neighbors Clarification Rule and Metrics_Old Kiwi|17]], [[Lecture 18 - Nearest Neighbors Clarification Rule and Metrics(Continued)_Old Kiwi|18]],
    8 KB (1,235 words) - 08:49, 17 January 2013
  • Lecture 13 - Kernel function for SVMs and ANNs introduction Old Kiwi
    [[Lecture 17 - Nearest Neighbors Clarification Rule and Metrics_Old Kiwi|17]], [[Lecture 18 - Nearest Neighbors Clarification Rule and Metrics(Continued)_Old Kiwi|18]],
    8 KB (1,354 words) - 08:51, 17 January 2013
  • Lecture 14 - ANNs, Non-parametric Density Estimation (Parzen Window) Old Kiwi
    [[Lecture 17 - Nearest Neighbors Clarification Rule and Metrics_Old Kiwi|17]], [[Lecture 18 - Nearest Neighbors Clarification Rule and Metrics(Continued)_Old Kiwi|18]],
    13 KB (2,073 words) - 08:39, 17 January 2013
  • Lecture 15 - Parzen Window Method Old Kiwi
    [[Lecture 17 - Nearest Neighbors Clarification Rule and Metrics_Old Kiwi|17]], [[Lecture 18 - Nearest Neighbors Clarification Rule and Metrics(Continued)_Old Kiwi|18]],
    7 KB (1,212 words) - 08:38, 17 January 2013
  • Lecture 16 - Parzen Window Method and K-nearest Neighbor Density Estimate Old Kiwi
    [[Lecture 17 - Nearest Neighbors Clarification Rule and Metrics_Old Kiwi|17]], [[Lecture 18 - Nearest Neighbors Clarification Rule and Metrics(Continued)_Old Kiwi|18]],
    10 KB (1,607 words) - 08:38, 17 January 2013
  • Lecture 18 - Nearest Neighbors Clarification Rule and Metrics(Continued) Old Kiwi
    [[Lecture 17 - Nearest Neighbors Clarification Rule and Metrics_Old Kiwi|17]], [[Lecture 18 - Nearest Neighbors Clarification Rule and Metrics(Continued)_Old Kiwi|18]],
    6 KB (1,066 words) - 08:40, 17 January 2013
  • ECE662:ChangeLog Old Kiwi
    ...he section on [[Lecture 3 - Bayes classification_Old Kiwi#Bayes_rule|Bayes rule]] equation <3,4,5> and figures <1,2,3>. ...Clustering Methods_Old Kiwi]] by adding the section on how the separation rule obtained by mixture of Gaussians model can be generalized to future unseen
    10 KB (1,418 words) - 12:21, 28 April 2008
  • Bayesian Decision Theory Old Kiwi
    ...PROBABILITY and LIKELIHOOD by forming a POSTERIOR probability using Bayes Rule.
    3 KB (558 words) - 17:03, 16 April 2008
  • ROC curves Old Kiwi
    ...ass 1 is more likely than class 2, and we select class 1. Applying Bayes' rule, and canceling the p(x):
    3 KB (621 words) - 08:48, 10 April 2008
  • Publications Old Kiwi
    ...any number of categories, the probability of error of the nearest neighbor rule is bounded above by twice the Bayes probability of error. In this sense, it ...al supervised neural-network training algorithms (including the perceptron rule, the least-mean-square algorithm, three Madaline rules, and the backpropaga
    39 KB (5,715 words) - 10:52, 25 April 2008
  • Lecture 4 - Bayes Classification Old Kiwi
    [[Lecture 17 - Nearest Neighbors Clarification Rule and Metrics_Old Kiwi|17]], [[Lecture 18 - Nearest Neighbors Clarification Rule and Metrics(Continued)_Old Kiwi|18]],
    8 KB (1,360 words) - 08:46, 17 January 2013
  • Bayes Decision Rule Old Kiwi
    =Bayes Decision Rule Video= The video demonstrates Bayes decision rule on 2D feature data from two classes. We visualize the decision hyper surfac
    1 KB (172 words) - 11:08, 10 June 2013
  • Lecture 19 - Nearest Neighbor Error Rates Old Kiwi
    [[Lecture 17 - Nearest Neighbors Clarification Rule and Metrics_Old Kiwi|17]], [[Lecture 18 - Nearest Neighbors Clarification Rule and Metrics(Continued)_Old Kiwi|18]],
    5 KB (1,003 words) - 08:40, 17 January 2013
  • Lecture 20 - Density Estimation using Series Expansion and Decision Trees Old Kiwi
    [[Lecture 17 - Nearest Neighbors Clarification Rule and Metrics_Old Kiwi|17]], [[Lecture 18 - Nearest Neighbors Clarification Rule and Metrics(Continued)_Old Kiwi|18]],
    6 KB (1,047 words) - 08:42, 17 January 2013
  • Lecture 21 - Decision Trees(Continued) Old Kiwi
    [[Lecture 17 - Nearest Neighbors Clarification Rule and Metrics_Old Kiwi|17]], [[Lecture 18 - Nearest Neighbors Clarification Rule and Metrics(Continued)_Old Kiwi|18]],
    6 KB (1,012 words) - 08:42, 17 January 2013
  • Lecture 22 - Decision Trees and Clustering Old Kiwi
    [[Lecture 17 - Nearest Neighbors Clarification Rule and Metrics_Old Kiwi|17]], [[Lecture 18 - Nearest Neighbors Clarification Rule and Metrics(Continued)_Old Kiwi|18]],
    6 KB (806 words) - 08:42, 17 January 2013
  • Bayes Example Old Kiwi
    ...PROBABILITY and LIKELIHOOD by forming a POSTERIOR probability using Bayes Rule.
    2 KB (302 words) - 01:09, 7 April 2008
  • Editing technique Old Kiwi
    ...each region, we can observe some samples which are misclassified by Bayes rule. Removing these misclassfied sample will generate two homogeneous sets of s The followings are the algorithm of the editing technique for the K-NN rule:
    2 KB (296 words) - 11:48, 7 April 2008
  • Lecture 23 - Spanning Trees Old Kiwi
    [[Lecture 17 - Nearest Neighbors Clarification Rule and Metrics_Old Kiwi|17]], [[Lecture 18 - Nearest Neighbors Clarification Rule and Metrics(Continued)_Old Kiwi|18]],
    7 KB (1,060 words) - 08:43, 17 January 2013
  • Lecture 24 - Clustering and Hierarchical Clustering Old Kiwi
    [[Lecture 17 - Nearest Neighbors Clarification Rule and Metrics_Old Kiwi|17]], [[Lecture 18 - Nearest Neighbors Clarification Rule and Metrics(Continued)_Old Kiwi|18]],
    8 KB (1,254 words) - 08:43, 17 January 2013
  • Lecture 25 - Clustering Algorithms Old Kiwi
    [[Lecture 17 - Nearest Neighbors Clarification Rule and Metrics_Old Kiwi|17]], [[Lecture 18 - Nearest Neighbors Clarification Rule and Metrics(Continued)_Old Kiwi|18]],
    8 KB (1,259 words) - 08:43, 17 January 2013
  • Bayes classification Old Kiwi
    == Bayes rule == Bayes rule addresses the predefined classes classification problem.
    2 KB (399 words) - 14:03, 18 June 2008
  • Lecture 26 - Statistical Clustering Methods Old Kiwi
    [[Lecture 17 - Nearest Neighbors Clarification Rule and Metrics_Old Kiwi|17]], [[Lecture 18 - Nearest Neighbors Clarification Rule and Metrics(Continued)_Old Kiwi|18]],
    8 KB (1,244 words) - 08:44, 17 January 2013
  • Lecture 27 - Clustering by finding valleys of densities Old Kiwi
    [[Lecture 17 - Nearest Neighbors Clarification Rule and Metrics_Old Kiwi|17]], [[Lecture 18 - Nearest Neighbors Clarification Rule and Metrics(Continued)_Old Kiwi|18]],
    8 KB (1,337 words) - 08:44, 17 January 2013
  • Lecture 28 - Final lecture Old Kiwi
    [[Lecture 17 - Nearest Neighbors Clarification Rule and Metrics_Old Kiwi|17]], [[Lecture 18 - Nearest Neighbors Clarification Rule and Metrics(Continued)_Old Kiwi|18]],
    10 KB (1,728 words) - 08:55, 17 January 2013
  • Lecture 2 - Decision Hypersurfaces OldKiwi
    [[Lecture 17 - Nearest Neighbors Clarification Rule and Metrics_OldKiwi|17]]| [[Lecture 18 - Nearest Neighbors Clarification Rule and Metrics(Continued)_OldKiwi|18]]|
    5 KB (744 words) - 11:17, 10 June 2013
  • ECE662:BoutinSpring08 OldKiwi
    ...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)]]
    7 KB (875 words) - 07:11, 13 February 2012
  • ECE662
    *[[Bayes_Rate_Fallacy:_Bayes_Rules_under_Severe_Class_Imbalance|Bayes rule under severe class imbalance]]
    3 KB (429 words) - 09:07, 11 January 2016
  • Test
    == '''2.1 Classifier using Bayes rule''' == ...{i} \mid x \big) </math>. So instead of solving eq.(2.1), we use the Bayes rule to change the problem to
    17 KB (2,590 words) - 10:45, 22 January 2015
  • Lecture 9 - 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]]|
    9 KB (1,341 words) - 11:15, 10 June 2013
  • 2010 Spring ECE 662 mboutin
    *[[Bayes_Rate_Fallacy:_Bayes_Rules_under_Severe_Class_Imbalance|Bayes rule under severe class imbalance]] *[[Hw1 ECE662Spring2010|HW1- Bayes rule for normally distributed features]]
    4 KB (547 words) - 12:24, 25 June 2010
  • 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
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    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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Alumni Liaison

Correspondence Chess Grandmaster and Purdue Alumni

Prof. Dan Fleetwood