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  • HKNQE Automatic Controls
    ==Optimization==
    214 B (21 words) - 11:35, 3 December 2008
  • Meta Course List
    *[[MA421|MA 421]]:"Linear Programming and Optimization Techniques"
    4 KB (474 words) - 07:08, 4 November 2013
  • ECE662:BoutinSpring08 Old Kiwi
    * [[Lecture 12 - Support Vector Machine and Quadratic Optimization Problem_Old Kiwi]]
    6 KB (747 words) - 05:18, 5 April 2013
  • ECE662:Glossary Old Kiwi
    ...to one or more constraints; it is the basic tool in nonlinear constrained optimization. Simply put, the technique is able to determine where on a particular set o ...roblems. It can be shown that the solutions of these derived unconstrained optimization problems will converge to the solution of the original constrained problem.
    31 KB (4,832 words) - 18:13, 22 October 2010
  • Clustering Old Kiwi
    * '''Optimization-based methods'''
    8 KB (1,173 words) - 12:41, 26 April 2008
  • Lecture 17 - Nearest Neighbors Clarification Rule and Metrics Old Kiwi
    [[Lecture 12 - Support Vector Machine and Quadratic Optimization Problem_Old Kiwi|12]],
    6 KB (938 words) - 08:38, 17 January 2013
  • Lecture 1 - Introduction Old Kiwi
    [[Lecture 12 - Support Vector Machine and Quadratic Optimization Problem_Old Kiwi|12]],
    3 KB (468 words) - 08:45, 17 January 2013
  • Lecture 2 - Decision Hypersurfaces Old Kiwi
    [[Lecture 12 - Support Vector Machine and Quadratic Optimization Problem_Old Kiwi|12]],
    5 KB (737 words) - 08:45, 17 January 2013
  • Lecture 3 - Bayes classification Old Kiwi
    [[Lecture 12 - Support Vector Machine and Quadratic Optimization Problem_Old Kiwi|12]],
    5 KB (843 words) - 08:46, 17 January 2013
  • Lecture 5 - Discriminant Functions Old Kiwi
    [[Lecture 12 - Support Vector Machine and Quadratic Optimization Problem_Old Kiwi|12]],
    6 KB (916 words) - 08:47, 17 January 2013
  • Lecture 6 - Discriminant Functions Old Kiwi
    [[Lecture 12 - Support Vector Machine and Quadratic Optimization Problem_Old Kiwi|12]],
    9 KB (1,586 words) - 08:47, 17 January 2013
  • Lecture 7 - MLE and BPE Old Kiwi
    [[Lecture 12 - Support Vector Machine and Quadratic Optimization Problem_Old Kiwi|12]],
    10 KB (1,488 words) - 10:16, 20 May 2013
  • Lecture 8 - MLE, BPE and Linear Discriminant Functions Old Kiwi
    [[Lecture 12 - Support Vector Machine and Quadratic Optimization Problem_Old Kiwi|12]],
    5 KB (792 words) - 08:48, 17 January 2013
  • Lecture 9 - Linear Discriminant Functions Old Kiwi
    [[Lecture 12 - Support Vector Machine and Quadratic Optimization Problem_Old Kiwi|12]],
    8 KB (1,307 words) - 08:48, 17 January 2013
  • Lecture 10 - Batch Perceptron and Fisher Linear Discriminant Old Kiwi
    [[Lecture 12 - Support Vector Machine and Quadratic Optimization Problem_Old Kiwi|12]],
    5 KB (755 words) - 08:48, 17 January 2013
  • Lecture 11 - Fischer's Linear Discriminant again Old Kiwi
    [[Lecture 12 - Support Vector Machine and Quadratic Optimization Problem_Old Kiwi|12]],
    5 KB (907 words) - 08:49, 17 January 2013
  • Lecture 12 - Support Vector Machine and Quadratic Optimization Problem Old Kiwi
    [[Lecture 12 - Support Vector Machine and Quadratic Optimization Problem_Old Kiwi|12]], This gives a [[Quadratic Optimization Problem_Old Kiwi]]: Minimize <math>\frac{1}{2}||\vec{c}||^2</math> subject
    8 KB (1,235 words) - 08:49, 17 January 2013
  • Lecture 13 - Kernel function for SVMs and ANNs introduction Old Kiwi
    [[Lecture 12 - Support Vector Machine and Quadratic Optimization Problem_Old Kiwi|12]], Previous: [[Lecture 12 - Support Vector Machine and Quadratic Optimization Problem_Old Kiwi]]; Next: [[Lecture 14 - ANNs, Non-parametric Density Estim
    8 KB (1,354 words) - 08:51, 17 January 2013
  • Lecture 14 - ANNs, Non-parametric Density Estimation (Parzen Window) Old Kiwi
    [[Lecture 12 - Support Vector Machine and Quadratic Optimization Problem_Old Kiwi|12]],
    13 KB (2,073 words) - 08:39, 17 January 2013
  • Lecture 15 - Parzen Window Method Old Kiwi
    [[Lecture 12 - Support Vector Machine and Quadratic Optimization Problem_Old Kiwi|12]],
    7 KB (1,212 words) - 08:38, 17 January 2013
  • Lecture 16 - Parzen Window Method and K-nearest Neighbor Density Estimate Old Kiwi
    [[Lecture 12 - Support Vector Machine and Quadratic Optimization Problem_Old Kiwi|12]],
    10 KB (1,607 words) - 08:38, 17 January 2013
  • Old QE question numbering Old Kiwi
    |ee580 || AC-3 || (AC-2) || Optimization
    2 KB (279 words) - 23:00, 9 March 2008
  • Lecture 18 - Nearest Neighbors Clarification Rule and Metrics(Continued) Old Kiwi
    [[Lecture 12 - Support Vector Machine and Quadratic Optimization Problem_Old Kiwi|12]],
    6 KB (1,066 words) - 08:40, 17 January 2013
  • Support Vector Machines Old Kiwi
    ...Old Kiwi|Lecture 11]], [[Lecture 12 - Support Vector Machine and Quadratic Optimization Problem_Old Kiwi|Lecture 12]] and [[Lecture 13 - Kernel function for SVMs a
    3 KB (366 words) - 08:48, 10 April 2008
  • ECE662:ChangeLog Old Kiwi
    * 2008/04/20 -- Added four papers in [[Publications_Old Kiwi]] about Optimization-based clustering methods.
    10 KB (1,418 words) - 12:21, 28 April 2008
  • Feature Extraction Old Kiwi
    A global optimization technique is introduced for statistical classifier design to minimize the p
    3 KB (430 words) - 10:40, 24 April 2008
  • Genetic algorithms Old Kiwi
    GA is widely used as a optimization technique. Other biological inspired techniques include Ant Colony Optimisa
    2 KB (254 words) - 22:51, 10 March 2008
  • Publications Old Kiwi
    ===Optimization-based clustering methods=== ...that follows the min-max clustering principle. The relaxed version of the optimization of the min-max cut objective function leads to the Fiedler vector in spectr
    39 KB (5,715 words) - 10:52, 25 April 2008
  • Derivation of Fisher's Linear Discriminant Old Kiwi
    Need to fix <math>|| \omega || = 1</math> , resulting in a Constrained Optimization Problem.
    4 KB (653 words) - 10:50, 22 April 2008
  • Lecture 4 - Bayes Classification Old Kiwi
    [[Lecture 12 - Support Vector Machine and Quadratic Optimization Problem_Old Kiwi|12]],
    8 KB (1,360 words) - 08:46, 17 January 2013
  • Genetic algorithm Old Kiwi
    ...cal inspired techniques include Ant Colony Optimization and Particle Swarm Optimization.
    2 KB (253 words) - 00:42, 22 March 2008
  • Gradient Descent Old Kiwi
    [[Category:Optimization]]
    1 KB (201 words) - 10:46, 24 March 2008
  • Lecture 19 - Nearest Neighbor Error Rates Old Kiwi
    [[Lecture 12 - Support Vector Machine and Quadratic Optimization Problem_Old Kiwi|12]],
    5 KB (1,003 words) - 08:40, 17 January 2013
  • Lecture 20 - Density Estimation using Series Expansion and Decision Trees Old Kiwi
    [[Lecture 12 - Support Vector Machine and Quadratic Optimization Problem_Old Kiwi|12]],
    6 KB (1,047 words) - 08:42, 17 January 2013
  • Lecture 21 - Decision Trees(Continued) Old Kiwi
    [[Lecture 12 - Support Vector Machine and Quadratic Optimization Problem_Old Kiwi|12]], Query selection => numerical optimization problem.
    6 KB (1,012 words) - 08:42, 17 January 2013
  • Lecture 22 - Decision Trees and Clustering Old Kiwi
    [[Lecture 12 - Support Vector Machine and Quadratic Optimization Problem_Old Kiwi|12]],
    6 KB (806 words) - 08:42, 17 January 2013
  • Lagrange Multipliers Old Kiwi
    ...to one or more constraints; it is the basic tool in nonlinear constrained optimization. Simply put, the technique is able to determine where on a particular set o
    448 B (72 words) - 10:19, 7 April 2008
  • Lecture 23 - Spanning Trees Old Kiwi
    [[Lecture 12 - Support Vector Machine and Quadratic Optimization Problem_Old Kiwi|12]],
    7 KB (1,060 words) - 08:43, 17 January 2013
  • Lecture 24 - Clustering and Hierarchical Clustering Old Kiwi
    [[Lecture 12 - Support Vector Machine and Quadratic Optimization Problem_Old Kiwi|12]],
    8 KB (1,254 words) - 08:43, 17 January 2013
  • Fuzzy C-means Old Kiwi
    ...d data and the center. Fuzzy partition is carried out through an iterative optimization of the previous expression with the update of membership <math>u_{ij}</math
    1 KB (258 words) - 16:20, 10 April 2008
  • Lecture 25 - Clustering Algorithms Old Kiwi
    [[Lecture 12 - Support Vector Machine and Quadratic Optimization Problem_Old Kiwi|12]], to speed up optimization of J
    8 KB (1,259 words) - 08:43, 17 January 2013
  • Penalty Methods Old Kiwi
    ...roblems. It can be shown that the solutions of these derived unconstrained optimization problems will converge to the solution of the original constrained problem.
    577 B (83 words) - 01:44, 17 April 2008
  • Lecture 26 - Statistical Clustering Methods Old Kiwi
    [[Lecture 12 - Support Vector Machine and Quadratic Optimization Problem_Old Kiwi|12]],
    8 KB (1,244 words) - 08:44, 17 January 2013
  • Lecture 27 - Clustering by finding valleys of densities Old Kiwi
    [[Lecture 12 - Support Vector Machine and Quadratic Optimization Problem_Old Kiwi|12]],
    8 KB (1,337 words) - 08:44, 17 January 2013
  • Lecture 28 - Final lecture Old Kiwi
    [[Lecture 12 - Support Vector Machine and Quadratic Optimization Problem_Old Kiwi|12]],
    10 KB (1,728 words) - 08:55, 17 January 2013
  • Quadratic Programming Problem Old Kiwi
    A linearly constrained optimization problem with a quadratic objective function is called a quadratic program (
    232 B (35 words) - 23:24, 24 April 2008
  • Slack Variable Old Kiwi
    ...equality constraint. This procedure is often performed to formulate linear optimization problems into a form which can be efficiently solve using a fast algorithm:
    474 B (75 words) - 23:25, 24 April 2008
  • Neural Networks using Genetic Algorithms Old Kiwi
    ...wiki/Genetic_algorithm) are a method of determining the best solutions for optimization and search problems by means of evolution using simulations. The steps are
    2 KB (288 words) - 11:51, 25 April 2008
  • Lecture 2 - Decision Hypersurfaces OldKiwi
    [[Lecture 12 - Support Vector Machine and Quadratic Optimization Problem_OldKiwi|12]]|
    5 KB (744 words) - 11:17, 10 June 2013
  • ECE662:BoutinSpring08 OldKiwi
    ...mization Problem_OldKiwi|Lecture 12 - Support Vector Machine and Quadratic Optimization Problem]]
    7 KB (875 words) - 07:11, 13 February 2012
  • Detailed Course Description
    Signals and Systems, 3rd edition, N. Levan, Optimization Software, Inc., New York, ISBN 0-911575-63-4, 1992.
    7 KB (1,153 words) - 14:06, 24 August 2009
  • Lecture 9 - Linear Discriminant Functions OldKiwi
    [[Lecture 12 - Support Vector Machine and Quadratic Optimization Problem_OldKiwi|12]]|
    9 KB (1,341 words) - 11:15, 10 June 2013
  • ECE662Sp10 MakeupLectureNotes01
    ...o solve. So, fix <math>|\vec{w}|=1</math>, but then this is a "constraint optimization problem" (did i screw up this description? --[[User:Mreeder|Mreeder]] 21:56
    9 KB (1,536 words) - 07:26, 12 April 2010
  • Lecture24ECE662S10
    ...s" in the case where the data is not linearly separable. We noted that the optimization problem in that case involves inner products between the training samples,
    1 KB (188 words) - 10:36, 16 April 2010
  • Noteslecture8ECE662S10
    ...alytically or numerically minimizing <math>e^{-f(\beta)}</math>, therefore optimization is now in the one dimensional β space.
    5 KB (806 words) - 09:08, 11 May 2010
  • MA421 Announcement Spring2011
    =[[MA421]]: "Linear Programming and Optimization Techniques"= "optimization problems". In all cases, one wants to minimize or maximize a
    2 KB (266 words) - 07:29, 4 January 2011
  • Decision Theory Glossary ECE662Spring2008
    ...to one or more constraints; it is the basic tool in nonlinear constrained optimization. Simply put, the technique is able to determine where on a particular set o ...roblems. It can be shown that the solutions of these derived unconstrained optimization problems will converge to the solution of the original constrained problem.
    31 KB (4,787 words) - 18:21, 22 October 2010
  • MA421
    =MA421: "Linear Programming and Optimization Techniques"=
    768 B (100 words) - 11:33, 11 November 2010
  • Linear programming MA265F11Walther
    ...lly use as one of the optimization problem solvers. In general, to solve a optimization problem, you need to have an objective (what do you want to solve? Do you w There are many software that help you solve optimization problems. In this page, I will focus on how to use GAMS.
    5 KB (736 words) - 09:14, 11 April 2013
  • Professional statement alex layton ECE400S12
    ...application of signal processing in agriculture, such as yield mapping or optimization of fertilizer or pesticide application. This research is supervised by Prof
    5 KB (721 words) - 12:18, 9 February 2012
  • Lecture 1 - Introduction OldKiwi
    [[Lecture 12 - Support Vector Machine and Quadratic Optimization Problem_OldKiwi|12]]|
    3 KB (413 words) - 11:17, 10 June 2013
  • Lecture 3 - Bayes classification OldKiwi
    [[Lecture 12 - Support Vector Machine and Quadratic Optimization Problem_OldKiwi|12]]|
    6 KB (874 words) - 11:17, 10 June 2013
  • Lecture 4 - Bayes Classification OldKiwi
    [[Lecture 12 - Support Vector Machine and Quadratic Optimization Problem_OldKiwi|12]]|
    8 KB (1,403 words) - 11:17, 10 June 2013
  • Lecture 16 - Parzen Window Method and K-nearest Neighbor Density Estimate OldKiwi
    [[Lecture 12 - Support Vector Machine and Quadratic Optimization Problem_OldKiwi|12]]|
    10 KB (1,609 words) - 11:22, 10 June 2013
  • Lecture 17 - Nearest Neighbors Clarification Rule and Metrics OldKiwi
    [[Lecture 12 - Support Vector Machine and Quadratic Optimization Problem_OldKiwi|12]]|
    6 KB (977 words) - 11:22, 10 June 2013
  • Lecture 18 - Nearest Neighbors Clarification Rule and Metrics(Continued) OldKiwi
    [[Lecture 12 - Support Vector Machine and Quadratic Optimization Problem_OldKiwi|12]]|
    7 KB (1,098 words) - 11:22, 10 June 2013
  • Lecture 6 - Discriminant Functions OldKiwi
    [[Lecture 12 - Support Vector Machine and Quadratic Optimization Problem_OldKiwi|12]]|
    10 KB (1,604 words) - 11:17, 10 June 2013
  • Lecture 7 - MLE and BPE OldKiwi
    [[Lecture 12 - Support Vector Machine and Quadratic Optimization Problem_OldKiwi|12]]|
    10 KB (1,472 words) - 11:16, 10 June 2013
  • Lecture 5 - Discriminant Functions OldKiwi
    [[Lecture 12 - Support Vector Machine and Quadratic Optimization Problem_OldKiwi|12]]|
    6 KB (946 words) - 11:17, 10 June 2013
  • Lecture 8 - MLE, BPE and Linear Discriminant Functions OldKiwi
    [[Lecture 12 - Support Vector Machine and Quadratic Optimization Problem_OldKiwi|12]]|
    6 KB (833 words) - 11:16, 10 June 2013
  • Lecture 10 - Batch Perceptron and Fisher Linear Discriminant OldKiwi
    [[Lecture 12 - Support Vector Machine and Quadratic Optimization Problem_OldKiwi|12]]|
    6 KB (813 words) - 11:18, 10 June 2013
  • Lecture 11 - Fischer's Linear Discriminant again OldKiwi
    [[Lecture 12 - Support Vector Machine and Quadratic Optimization Problem_OldKiwi|12]]|
    6 KB (946 words) - 11:18, 10 June 2013
  • Lecture 12 - Support Vector Machine and Quadratic Optimization Problem OldKiwi
    [[Lecture 12 - Support Vector Machine and Quadratic Optimization Problem_OldKiwi|12]]| This gives a [[Quadratic Optimization Problem_OldKiwi]]: Minimize <math>\frac{1}{2}||\vec{c}||^2</math> subject t
    8 KB (1,278 words) - 11:19, 10 June 2013
  • Lecture 13 - Kernel function for SVMs and ANNs introduction OldKiwi
    [[Lecture 12 - Support Vector Machine and Quadratic Optimization Problem_OldKiwi|12]]| (continued from [[Lecture 12 - Support Vector Machine and Quadratic Optimization Problem_OldKiwi|Lecture 12]])
    9 KB (1,389 words) - 11:19, 10 June 2013
  • Lecture 14 - ANNs, Non-parametric Density Estimation (Parzen Window) OldKiwi
    [[Lecture 12 - Support Vector Machine and Quadratic Optimization Problem_OldKiwi|12]]|
    13 KB (2,098 words) - 11:21, 10 June 2013
  • Lecture 15 - Parzen Window Method OldKiwi
    [[Lecture 12 - Support Vector Machine and Quadratic Optimization Problem_OldKiwi|12]]|
    8 KB (1,246 words) - 11:21, 10 June 2013
  • Lecture 19 - Nearest Neighbor Error Rates OldKiwi
    [[Lecture 12 - Support Vector Machine and Quadratic Optimization Problem_OldKiwi|12]]|
    6 KB (1,041 words) - 11:22, 10 June 2013
  • Lecture 20 - Density Estimation using Series Expansion and Decision Trees OldKiwi
    [[Lecture 12 - Support Vector Machine and Quadratic Optimization Problem_OldKiwi|12]]|
    7 KB (1,082 words) - 11:23, 10 June 2013
  • Lecture 21 - Decision Trees(Continued) OldKiwi
    [[Lecture 12 - Support Vector Machine and Quadratic Optimization Problem_OldKiwi|12]]| Query selection => numerical optimization problem.
    7 KB (1,055 words) - 11:23, 10 June 2013
  • Lecture 22 - Decision Trees and Clustering OldKiwi
    [[Lecture 12 - Support Vector Machine and Quadratic Optimization Problem_OldKiwi|12]]|
    6 KB (837 words) - 11:23, 10 June 2013
  • Lecture 23 - Spanning Trees OldKiwi
    [[Lecture 12 - Support Vector Machine and Quadratic Optimization Problem_OldKiwi|12]]|
    7 KB (1,091 words) - 11:23, 10 June 2013
  • Lecture 24 - Clustering and Hierarchical Clustering OldKiwi
    [[Lecture 12 - Support Vector Machine and Quadratic Optimization Problem_OldKiwi|12]]|
    9 KB (1,276 words) - 11:24, 10 June 2013
  • Lecture 25 - Clustering Algorithms OldKiwi
    [[Lecture 12 - Support Vector Machine and Quadratic Optimization Problem_OldKiwi|12]]| to speed up optimization of J
    8 KB (1,299 words) - 11:24, 10 June 2013
  • Lecture 26 - Statistical Clustering Methods OldKiwi
    [[Lecture 12 - Support Vector Machine and Quadratic Optimization Problem_OldKiwi|12]]|
    8 KB (1,214 words) - 11:24, 10 June 2013
  • Lecture 27 - Clustering by finding valleys of densities OldKiwi
    [[Lecture 12 - Support Vector Machine and Quadratic Optimization Problem_OldKiwi|12]]|
    8 KB (1,313 words) - 11:24, 10 June 2013
  • Lecture 28 - Final lecture OldKiwi
    [[Lecture 12 - Support Vector Machine and Quadratic Optimization Problem_OldKiwi|12]]|
    10 KB (1,704 words) - 11:25, 10 June 2013
  • Greedy methods MA375S12Walther
    *Often we are looking at optimization problems whose performance is exponential. ...ints are called feasible solutions.<br>o A feasible solution for which the optimization function has the best possible value<br>is called an optimal solution.
    10 KB (1,828 words) - 07:01, 21 March 2013
  • ECE-QE AC3-2011 solusion-1
    [[Category:optimization]] ...th>\color{blue}\text{1. } \left( \text{20 pts} \right) \text{ Consider the optimization problem, }</math></span></font>
    11 KB (1,693 words) - 10:09, 13 September 2013
  • ECE PhD Qualifying Exams
    **Question 3: Optimization
    8 KB (952 words) - 22:00, 1 August 2019
  • ECE-QE AC3-2011 solusion-2
    [[Category:optimization]]
    9 KB (1,284 words) - 10:10, 13 September 2013
  • ECE-QE AC3-2011 solusion-3
    [[Category:optimization]]
    8 KB (1,062 words) - 10:10, 13 September 2013
  • ECE-QE AC3-2011 solusion-4
    [[Category:optimization]]
    8 KB (1,179 words) - 10:10, 13 September 2013
  • ECE-QE AC3-2011 solusion-5
    [[Category:optimization]] ...blue}\text{5. } \left( \text{20 pts} \right) \text{ Consider the following optimization problem, }</math></span></font>
    17 KB (2,526 words) - 10:11, 13 September 2013
  • ECE-QE AC3-2011
    [[Category:optimization]] Question 3: Optimization
    6 KB (855 words) - 10:17, 13 September 2013
  • QE2012 AC-3 ECE580
    [[Category:optimization]] Question 3: Optimization
    4 KB (528 words) - 10:17, 13 September 2013
  • QE2012 AC-3 ECE580-1
    [[Category:optimization]]
    2 KB (331 words) - 10:11, 13 September 2013
  • QE2012 AC-3 ECE580-2
    [[Category:optimization]]
    11 KB (1,373 words) - 10:12, 13 September 2013
  • QE2012 AC-3 ECE580-3
    [[Category:optimization]]
    4 KB (418 words) - 10:12, 13 September 2013
  • QE2012 AC-3 ECE580-4
    [[Category:optimization]]
    5 KB (593 words) - 10:12, 13 September 2013
  • QE2012 AC-3 ECE580-5
    [[Category:optimization]]
    10 KB (1,406 words) - 10:12, 13 September 2013
  • ECE662 Pattern Recognition Decision Making Processes Spring2008 sLecture collective
    ...mization Problem_OldKiwi|Lecture 12 - Support Vector Machine and Quadratic Optimization Problem]]
    3 KB (425 words) - 09:59, 4 November 2013
  • Applications of Poisson Random Variables
    ===Optimization=== ...ables are often used to model scenarios used to generate cost functions in optimization problems. For example, the economic lot scheduling problem aims to optimiz
    5 KB (708 words) - 07:22, 22 April 2013
  • Walther375Spring2014 P=NP and complexity of algorithms
    ...y to most of data encryption, the solution to many of scientific problems, optimization of routes such as for market strategies and lest we forget, a $1,000,000 pr
    13 KB (2,101 words) - 13:55, 27 April 2014
  • Walther375Spring2014 lows and cuts in graphs: Menger's theorem
    ...ear Programming Interpretation of Max-Flow Min-Cut Theorem". Combinatorial Optimization: Networks and Matroids. Dover. pp. 117–120. ISBN 0-486-41453-1.
    6 KB (1,108 words) - 08:29, 27 April 2014
  • HWK4MA530Spring2014
    ...stimates as a function of one real variable and then use standard calculus optimization techniques to find the minimum. Anyone have a different idea? Any slick pro
    4 KB (620 words) - 13:10, 18 February 2014
  • Upper Bounds for Bayes Error
    Although, this is not necessarily an easy problem to solve, because the optimization problem might not be convex and might have local minimums. For the purpose
    13 KB (2,062 words) - 10:45, 22 January 2015
  • SlectureKeehwanECE662Spring14
    ==Part 4: Summary of MLE and Numerical Optimization Options== ...ures2012/Lesson07_Optim.pdf Lesson 7 Intractable MLEs: Basics of Numerical Optimization]," "Statistical Modeling", University of Illinois at Urbana-Champaign.
    3 KB (427 words) - 10:50, 22 January 2015
  • KnnDensityEstimation
    ...ive for a large number of samples.But there are several pre-processing and optimization techniques to improve the efficiency of K-NN.
    10 KB (1,743 words) - 10:54, 22 January 2015
  • Least Squares Support Vector Machine and its Applications in Solving Linear Regression Problems
    ...texhtml"> | | ''c'' | | <sup>2</sup></span>. This leads to the following optimization problem:<br> ...</span>&nbsp;and <math>\textbf{b}</math>. Other than the parameters in the optimization problem, the SVM has another set of parameters called hyperparameters, incl
    14 KB (2,241 words) - 10:56, 22 January 2015
  • CBR logistic regression
    After some fitting optimization algorithm, the curve looks like the following: ...to zero, there is no close form solution, so we are going to use numerical optimization method to maximized the likelihood, namely, Newton's method.
    9 KB (1,540 words) - 10:56, 22 January 2015
  • SlectureKeehwanECE662Spring14Review
    ...ore easily understand the properties of MLE. And I wish that the numerical optimization of MLE should have been explained in detail.
    1 KB (184 words) - 11:36, 2 May 2014
  • MLEforGMM
    &nbsp; Basic optimization theories show that α_j is optimized by:
    13 KB (1,966 words) - 10:50, 22 January 2015
  • Review on Support Vector Machine
    ...assification. He also gives a concise breakdown of SVM in the language of optimization. ...s especially true for the kernel trick, when one sees it in the context of optimization.
    2 KB (291 words) - 07:18, 8 May 2014
  • SlectureDavidRunyanCS662Spring14
    ...ath>J</math> for each <math>n</math> are independent of each other, so the optimization is rather simple. Assign each data point to the center that gives the minim In simplest terms, this optimization step can be summarized as "assign each data point to the closest center".
    8 KB (1,350 words) - 10:57, 22 January 2015
  • QE2013 AC-3 ECE580
    [[Category:optimization]] Question 3: Optimization
    4 KB (642 words) - 12:23, 25 March 2015
  • QE2013 AC-3 ECE580-3
    [[Category:optimization]]
    8 KB (1,016 words) - 12:19, 25 March 2015
  • QE2013 AC-3 ECE580-2
    [[Category:optimization]]
    2 KB (339 words) - 12:13, 25 March 2015
  • QE2013 AC-3 ECE580-4
    [[Category:optimization]]
    2 KB (330 words) - 12:21, 25 March 2015
  • QE2013 AC-3 ECE580-5
    [[Category:optimization]]
    2 KB (330 words) - 12:22, 25 March 2015
  • QE2013 AC-3 ECE580-1
    [[Category:optimization]]
    2 KB (400 words) - 12:10, 25 March 2015
  • Walther MA279 Spring2016 topic2
    === <small>Bird Flocking/Partical Swarm Optimization</small> === ...ring. In Karaboga’s original paper, he used the algorithm in a numerical optimization problem.
    14 KB (2,177 words) - 11:28, 24 April 2016
  • Walther MA279 Spring2016 topic11
    ...e trying to calculate? In simplest terms, the transportation problem is an optimization problem. In our case, we want to get good from the warehouses to the outlet
    15 KB (2,613 words) - 22:35, 24 April 2016
  • ECE-QE CE1-2013
    Describe a dynamic programming formulation to find a solution for this optimization problem. Compute the complexity of solving your dynamic programming formula
    4 KB (668 words) - 20:23, 21 August 2017
  • ECE PhD QE CE 2013 Problem1.2
    Describe a dynamic programming formulation to find a solution for this optimization problem. Compute the complexity of solving your dynamic programming formula
    4 KB (667 words) - 16:09, 23 August 2017
  • ECE PhD QE CE 2013 Problem1.4
    ...mization problem in computer science and is a typical example of a NP-hard optimization problem. Its decision version, the vertex cover problem, is one of [https:/
    6 KB (995 words) - 16:05, 23 August 2017
  • MA279Fall2018Topic1 Flowers
    ...e way the seeds are packed into the seed head. Fibonacci spirals allow for optimization in the way the seeds are packed. The seeds will always be uniformly packed
    2 KB (286 words) - 17:09, 2 December 2018
  • QE2017 AC-3 ECE580
    [[Category:optimization]] Question 3: Optimization
    6 KB (899 words) - 01:04, 24 February 2019
  • QE2016 AC-3 ECE580
    [[Category:optimization]] Question 3: Optimization
    3 KB (433 words) - 16:15, 19 February 2019
  • QE2010 CS-2 ECE538
    [[Category:optimization]]
    8 KB (1,474 words) - 16:37, 24 February 2019
  • QE2007 AC-1 ECE382
    [[Category:optimization]]
    7 KB (1,007 words) - 00:58, 24 February 2019
  • QE2011 CS-2 ECE538
    [[Category:optimization]]
    4 KB (738 words) - 15:34, 19 February 2019
  • 2016AC-3-1
    Question 3: Optimization
    2 KB (211 words) - 11:37, 25 February 2019
  • 2016AC-3-2
    Question 3: Optimization a) From the Optimization textbook, Zak Stanislaw. Lemma 8.3<br>
    769 B (106 words) - 16:19, 19 February 2019
  • 2016AC-3-3
    Question 3: Optimization
    1 KB (214 words) - 11:42, 25 February 2019
  • 2016AC-3-4
    Question 3: Optimization
    1 KB (178 words) - 11:45, 25 February 2019
  • 2016AC-3-5
    Question 3: Optimization
    2 KB (247 words) - 11:48, 25 February 2019
  • QE2007 CS-4 ECE580
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    5 KB (910 words) - 03:02, 24 February 2019
  • Walther MA271 Fall2020 topic24
    ...definite programming is studied not in computer science, but is applied in optimization and engineering. Semidefinite programming is a new, growing field which is
    16 KB (2,725 words) - 00:24, 7 December 2020
  • Sphere Packing 8: Works Cited
    Wolchover, N. (2013, December 20). Mathematical Progress on Sphere-Packing Optimization Problems. Retrieved December 06, 2020, from ...https://www.quantamagazine.org/mathematical-progress-on-sphere-packing-optimization-problems-20131220/
    1 KB (136 words) - 11:29, 6 December 2020
  • Swanson MA279 Fall2022 topic12
    ...and promoted it more. However, due to manipulation of SEO, (Search Engine Optimization is the process of improving the quality and quantity of website traffic to ...l portfolios footsteps. With an additional concept called “search engine optimization,” also known as SEO, which is a set of practices that allows companies an
    21 KB (3,098 words) - 00:24, 1 December 2022

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Alumni Liaison

Correspondence Chess Grandmaster and Purdue Alumni

Prof. Dan Fleetwood