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Expected Value of MLE estimate over standard deviation and expected deviation

A slecture by ECE student Zhenpeng Zhao

Partly based on the ECE662 Spring 2014 lecture material of Prof. Mireille Boutin.




1. Motivation

  • Most likely converge as number of number of training sample increase.
  • Simpler than alternate methods such as Bayesian technique.



2. Motivation

  • Statistical Density Theory Context
    • Given c classes + some knowledge about features $ x \in \mathbb{R}^n<math> (or some other space) [[Image:Zhenpeng_Selecture_1.png]] [[Image:Zhenpeng_Selecture_2.png]] [[Image:Zhenpeng_Selecture_3.png]] [[Image:Zhenpeng_Selecture_4.png]] [[Image:Zhenpeng_Selecture_5.png]] ---- (create a question page and put a link below) == [[ECE662Selecture_ZHenpengMLE_Ques|Questions and comments]] == If you have any questions, comments, etc. please post them on [[ECE662Selecture_ZHenpengMLE_Ques|https://kiwi.ecn.purdue.edu/rhea/index.php/ECE662Selecture_ZHenpengMLE_Ques]]. ---- [[2014 Spring ECE 662 Boutin|Back to ECE662, Spring 2014]] [[Category:Slecture]] [[Category:ECE662Spring2014Boutin]] [[Category:ECE]] [[Category:ECE662]] [[Category:Pattern_recognition]] $

Alumni Liaison

Ph.D. 2007, working on developing cool imaging technologies for digital cameras, camera phones, and video surveillance cameras.

Buyue Zhang