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- 00:54, 17 April 2008 (diff | hist) . . (+1,182) . . N Parzen Window Old Kiwi (New page: Parzen windows are very similar to K nearest neighborhoods(KNN). Both methods can generate very complex decision boundaries. The main difference is that instead of looking at the k closest...) (current)
- 00:54, 17 April 2008 (diff | hist) . . (+1,182) . . N Parzen Window OldKiwi (New page: Parzen windows are very similar to K nearest neighborhoods(KNN). Both methods can generate very complex decision boundaries. The main difference is that instead of looking at the k closest...) (current)
- 00:51, 17 April 2008 (diff | hist) . . (+1) . . ECE662:Glossary Old Kiwi
- 00:51, 17 April 2008 (diff | hist) . . (+1) . . ECE662:Glossary OldKiwi
- 00:50, 17 April 2008 (diff | hist) . . (+594) . . ECE662:ChangeLog Old Kiwi (→The ChangeLog)
- 00:50, 17 April 2008 (diff | hist) . . (+578) . . ECE662:ChangeLog OldKiwi (→The ChangeLog)
- 00:45, 17 April 2008 (diff | hist) . . (+657) . . N Principal Component Analysis Old Kiwi (New page: Constructing new features which are the principal components of a data set. The principal components are random variables of maximal variance constructed from linear combinations of the in...) (current)
- 00:45, 17 April 2008 (diff | hist) . . (+657) . . N Principal Component Analysis OldKiwi (New page: Constructing new features which are the principal components of a data set. The principal components are random variables of maximal variance constructed from linear combinations of the in...) (current)
- 00:44, 17 April 2008 (diff | hist) . . (+71) . . N Posterior OldKiwi (New page: The probability, using prior knowledge, that a case belongs to a group.) (current)
- 00:44, 17 April 2008 (diff | hist) . . (+71) . . N Posterior Old Kiwi (New page: The probability, using prior knowledge, that a case belongs to a group.) (current)
- 00:44, 17 April 2008 (diff | hist) . . (+577) . . N Penalty Methods Old Kiwi (New page: In optimization, penalty methods are used to reformulate a constraint optimization problem into several unconstrained optimization problems. It can be shown that the solutions of these der...) (current)
- 00:44, 17 April 2008 (diff | hist) . . (+577) . . N Penalty Methods OldKiwi (New page: In optimization, penalty methods are used to reformulate a constraint optimization problem into several unconstrained optimization problems. It can be shown that the solutions of these der...) (current)
- 00:44, 17 April 2008 (diff | hist) . . (+1,179) . . N Pazen Window Old Kiwi (New page: Parzen windows are very similar to K nearest neighborhoods(KNN). Both methods can generate very complex decision boundaries. The main difference is that instead of looking at the k closest...) (current)
- 00:44, 17 April 2008 (diff | hist) . . (+1,179) . . N Pazen Window OldKiwi (New page: Parzen windows are very similar to K nearest neighborhoods(KNN). Both methods can generate very complex decision boundaries. The main difference is that instead of looking at the k closest...) (current)
- 00:43, 17 April 2008 (diff | hist) . . (+71) . . N Patterns Old Kiwi (New page: The items that we are trying to classify are vectors of these features.) (current)
- 00:43, 17 April 2008 (diff | hist) . . (+71) . . N Patterns OldKiwi (New page: The items that we are trying to classify are vectors of these features.) (current)
- 00:43, 17 April 2008 (diff | hist) . . (+165) . . N Parametric Classifiers Old Kiwi (New page: We find parametric decision boundaries to approximate true decision boundaries between classes. (Discussed in Lecture 9 - Linear Discriminant Functions)) (current)
- 00:43, 17 April 2008 (diff | hist) . . (+164) . . N Parametric Classifiers OldKiwi (New page: We find parametric decision boundaries to approximate true decision boundaries between classes. (Discussed in Lecture 9 - Linear Discriminant Functions)) (current)
- 00:43, 17 April 2008 (diff | hist) . . (+343) . . N Parametric Model Old Kiwi (New page: A parametric model is a set of related mathematical equations in which alternative scenarios are defined by changing the assumed values of a set of fixed coefficients (parameters). In stat...) (current)
- 00:43, 17 April 2008 (diff | hist) . . (+343) . . N Parametric Model OldKiwi (New page: A parametric model is a set of related mathematical equations in which alternative scenarios are defined by changing the assumed values of a set of fixed coefficients (parameters). In stat...) (current)
- 00:42, 17 April 2008 (diff | hist) . . (+197) . . N Parameter Estimation Old Kiwi (New page: Density estimation when the density is assumed to be in a specific parametric family. Special cases include maximum likelihood, maximum a posteriori, unbiased estimation, and predictive es...) (current)
- 00:42, 17 April 2008 (diff | hist) . . (+197) . . N Parameter Estimation OldKiwi (New page: Density estimation when the density is assumed to be in a specific parametric family. Special cases include maximum likelihood, maximum a posteriori, unbiased estimation, and predictive es...) (current)
- 00:42, 17 April 2008 (diff | hist) . . (+637) . . N Overfitting Old Kiwi (New page: In statistics, overfitting means that some of the relationships that appear statistically significant are actually just noise. A model with overfitting has much more freedom degrees than t...) (current)
- 00:42, 17 April 2008 (diff | hist) . . (+637) . . N Overfitting OldKiwi (New page: In statistics, overfitting means that some of the relationships that appear statistically significant are actually just noise. A model with overfitting has much more freedom degrees than t...) (current)
- 00:42, 17 April 2008 (diff | hist) . . (+319) . . N Non-parametric Model Old Kiwi (New page: Non-parametric models differ from parametric models in that the model structure is not specified a priori but is instead determined from data. The term nonparametric is not meant to imply ...) (current)
- 00:42, 17 April 2008 (diff | hist) . . (+319) . . N Non-parametric Model OldKiwi (New page: Non-parametric models differ from parametric models in that the model structure is not specified a priori but is instead determined from data. The term nonparametric is not meant to imply ...) (current)
- 00:42, 17 April 2008 (diff | hist) . . (+185) . . N Nonparametric regression/density estimation Old Kiwi (New page: An approach to regression/density estimation that doesn't require much prior knowledge but only a large amount of data. This includes histograms, kernel smoothing, and nearest-neighbor.) (current)
- 00:42, 17 April 2008 (diff | hist) . . (+185) . . N Nonparametric regression/density estimation OldKiwi (New page: An approach to regression/density estimation that doesn't require much prior knowledge but only a large amount of data. This includes histograms, kernel smoothing, and nearest-neighbor.) (current)
- 00:41, 17 April 2008 (diff | hist) . . (+398) . . N Minkowski Metric Old Kiwi (New page: The k-Minkowski metric between two points <math>P_1 = (x_1,x_2,...,x_n)</math> and <math>P_2 = (y_1,y_2,...,y_n)</math> is defined as <math> d_k = (\sum_{i=1}^n \parallel x_i-y_i \parallel...) (current)
- 00:41, 17 April 2008 (diff | hist) . . (+398) . . N Minkowski Metric OldKiwi (New page: The k-Minkowski metric between two points <math>P_1 = (x_1,x_2,...,x_n)</math> and <math>P_2 = (y_1,y_2,...,y_n)</math> is defined as <math> d_k = (\sum_{i=1}^n \parallel x_i-y_i \parallel...) (current)
- 00:41, 17 April 2008 (diff | hist) . . (+33) . . N MLE Old Kiwi (New page: See Maximum Likelihood Estimation) (current)
- 00:41, 17 April 2008 (diff | hist) . . (+33) . . N MLE OldKiwi (New page: See Maximum Likelihood Estimation) (current)
- 00:40, 17 April 2008 (diff | hist) . . (+257) . . N Manhattan Distance Old Kiwi (New page: Also known as taxicab metric. The Manhattan distance between two points (X,Y) in a cartesian system is defined as <math>dist(X,Y)=\sum_{i=1}^n{|x_i-y_i|}</math>. This is equal to the lengt...) (current)
- 00:40, 17 April 2008 (diff | hist) . . (+257) . . N Manhattan Distance OldKiwi (New page: Also known as taxicab metric. The Manhattan distance between two points (X,Y) in a cartesian system is defined as <math>dist(X,Y)=\sum_{i=1}^n{|x_i-y_i|}</math>. This is equal to the lengt...) (current)
- 00:40, 17 April 2008 (diff | hist) . . (+340) . . N Linear Discriminant Functions (LDF) Old Kiwi (New page: Functions that are linear combinations of x. <math>g(x) = w^t x + w_0 </math> Where <math>w</math> is the weight vector and <math>w_0</math> is the bias as threshold. In the two category ...) (current)
- 00:40, 17 April 2008 (diff | hist) . . (+340) . . N Linear Discriminant Functions (LDF) OldKiwi (New page: Functions that are linear combinations of x. <math>g(x) = w^t x + w_0 </math> Where <math>w</math> is the weight vector and <math>w_0</math> is the bias as threshold. In the two category ...) (current)
- 09:23, 7 April 2008 (diff | hist) . . (+405) . . ECE662:ChangeLog Old Kiwi (→The ChangeLog)
- 09:23, 7 April 2008 (diff | hist) . . (+394) . . ECE662:ChangeLog OldKiwi (→The ChangeLog)
- 09:19, 7 April 2008 (diff | hist) . . (+233) . . N LUT - Look-Up Table Old Kiwi (New page: In classification applications, LUT's can be used for comparing decisions, as long as memory is available. This method requires a discrete feature space that is reasonably small. (This met...) (current)
- 09:19, 7 April 2008 (diff | hist) . . (+233) . . N LUT - Look-Up Table OldKiwi (New page: In classification applications, LUT's can be used for comparing decisions, as long as memory is available. This method requires a discrete feature space that is reasonably small. (This met...) (current)
- 09:19, 7 April 2008 (diff | hist) . . (+448) . . N Lagrange Multipliers Old Kiwi (New page: In mathematical optimization problems, the method of Lagrange multipliers, named after Joseph Louis Lagrange, is a method for finding the extrema of a function of several variables subject...) (current)
- 09:19, 7 April 2008 (diff | hist) . . (+448) . . N Lagrange Multipliers OldKiwi (New page: In mathematical optimization problems, the method of Lagrange multipliers, named after Joseph Louis Lagrange, is a method for finding the extrema of a function of several variables subject...) (current)
- 09:18, 7 April 2008 (diff | hist) . . (+413) . . N Kernel Functions Old Kiwi (New page: These functions operate in the feature space without ever computing the coordinates of the data in that space, but rather by simply computing the inner products between the mappings of all...) (current)
- 09:18, 7 April 2008 (diff | hist) . . (+413) . . N Kernel Functions OldKiwi (New page: These functions operate in the feature space without ever computing the coordinates of the data in that space, but rather by simply computing the inner products between the mappings of all...) (current)
- 09:18, 7 April 2008 (diff | hist) . . (+321) . . N Informative Prior Old Kiwi (New page: In the Bayesian framework, the prior distribution of the variable is called an informative prior if it provide some specific information about the variable. Informative priors allows to in...) (current)
- 09:18, 7 April 2008 (diff | hist) . . (+321) . . N Informative Prior OldKiwi (New page: In the Bayesian framework, the prior distribution of the variable is called an informative prior if it provide some specific information about the variable. Informative priors allows to in...) (current)
- 09:17, 7 April 2008 (diff | hist) . . (+248) . . N Impurity Old Kiwi (New page: Impurity of a class is defined as the amount of data misclassified into that class. It is zero when all training data belongs to one class. See Lecture 21 - Decision Trees(Continued) f...) (current)
- 09:17, 7 April 2008 (diff | hist) . . (+247) . . N Impurity OldKiwi (New page: Impurity of a class is defined as the amount of data misclassified into that class. It is zero when all training data belongs to one class. See Lecture 21 - Decision Trees(Continued) f...) (current)
- 09:16, 7 April 2008 (diff | hist) . . (+503) . . N Histogram Density Estimation Old Kiwi (New page: Histogram Density Estimation is one of the primitive and easiest non-parametric density estimation methods. The given feature space is divided into equally-spaced bins or cells. The number...) (current)
- 09:16, 7 April 2008 (diff | hist) . . (+503) . . N Histogram Density Estimation OldKiwi (New page: Histogram Density Estimation is one of the primitive and easiest non-parametric density estimation methods. The given feature space is divided into equally-spaced bins or cells. The number...) (current)
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