- published: 27 Jan 2015
- views: 2290
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This is a slecture for Prof. Boutin's course on Statistical Pattern Recognition (ECE662) made by Purdue student Nusaybah Amneh Abu-Mulaweh. The complete slecture is posted at https://www.projectrhea.org/rhea/index.php/Introduction_to_local_density_estimation_methods_ECE662_Spring2014_Nusaybah/Slecture_optimality_bayes_decision_rule_michaux_ECE662S14
To view other slectures on the same topic go to the ECE662 course wiki at https://www.projectrhea.org/rhea/index.php/2014_Spring_ECE_662_Boutin
For more information about slectures, go to http://slectures.projectrhea.org
- published: 05 May 2014
- views: 826
This is the introduction for non-parametric density estimation in ECE662.
- published: 01 May 2014
- views: 984
This video is part of an online course, Model Building and Validation. Check out the course here: https://www.udacity.com/course/ud919.
- published: 23 Feb 2015
- views: 1510
Covers basic ideas of nonparametric estimation, kernel density estimation, kernel regression, uncertainty calculations in kernel regression models, and bandwidth selection. Uploaded by Justin Esarey, Rice University, on 1/22/2014.
- published: 23 Jan 2014
- views: 11100
Pattern Recognition and Application by Prof. P.K. Biswas,Department of Electronics & Communication Engineering,IIT Kharagpur.For more details on NPTEL visit http://nptel.ac.in
- published: 02 Jun 2014
- views: 3056
This presentation provides an introduction to kernel density estimation (KDE) techniques for visualizing event densities in maps.
This video presentation was created as part of a peer-reviewed educational series on Spatial Analysis. The full suite of presentations can be accessed at http://ecolearnit.ifas.ufl.edu/. For more information on the statewide and online Geomatics program at the University of Florida, please visit us at http://flrec.ifas.ufl.edu/geomatics/ (Fort Lauderdale campus) or http://sfrc.ifas.ufl.edu/geomatics/ (Gainesville campus).
- published: 29 Jan 2016
- views: 357
The Pattern Recognition Class 2012 by Prof. Fred Hamprecht. It took place at the HCI / University of Heidelberg during the summer term of 2012.
Website: http://hci.iwr.uni-heidelberg.de/MIP/Teaching/pr/
Playlist with all videos: http://goo.gl/gmOI6
Contents of this recording:
density estimation
outlier detection
nonparametric classification
nonparametric regression
cluster analysis
mode hunting
average shifted histogram (ASH)
kernel density estimation (KDE)
Epanechnikov kernel
box kernel
b-spline kernel
Syllabus:
1. Introduction
1.1 Applications of Pattern Recognition
1.2 k-Nearest Neighbors Classification
1.3 Probability Theory
1.4 Statistical Decision Theory
2. Correlation measures, Gaussian Models
2.1 Pearson Correlation
2.2 Alternative Correlation Measures
2.3 Gaussian Graphical Models
2.4 Discriminant Analysis
3. Dimensionality Reduction
3.1 Regularized LDA/QDA
3.2 Principal Component Analysis (PCA)
3.3 Bilinear Decompositions
4. Neural Networks
4.1 History of Neural Networks
4.2 Perceptrons
4.3 Multilayer Perceptrons
4.4 The Projection Trick
4.5 Radial Basis Function Networks
5. Support Vector Machines
5.1 Loss Functions
5.2 Linear Soft-Margin SVM
5.3 Nonlinear SVM
6. Kernels, Random Forest
6.1 Kernels
6.2 One-Class SVM
6.3 Random Forest
6.4 Random Forest Feature Importance
7. Regression
7.1 Least-Squares Regression
7.2 Optimum Experimental Design
7.3 Case Study: Functional MRI
7.4 Case Study: Computer Tomography
7.5 Regularized Regression
8. Gaussian Processes
8.1 Gaussian Process Regression
8.2 GP Regression: Interpretation
8.3 Gaussian Stochastic Processes
8.4 Covariance Function
9. Unsupervised Learning
9.1 Kernel Density Estimation
9.2 Cluster Analysis
9.3 Expectation Maximization
9.4 Gaussian Mixture Models
10. Directed Graphical Models
10.1 Bayesian Networks
10.2 Variable Elimination
10.3 Message Passing
10.4 State Space Models
11. Optimization
11.1 The Lagrangian Method
11.2 Constraint Qualifications
11.3 Linear Programming
11.4 The Simplex Algorithm
12. Structured Learning
12.1 structSVM
12.2 Cutting Planes
- published: 22 Nov 2012
- views: 12966
In this tutorial, we'll continue trying to infer the probability density function of a random variable, but we'll use another method, called kernel density estimation.
Histogram video: http://www.youtube.com/watch?v=3wbDTuxq1Ac
For more information on kernel density estimation, visit http://bit.ly/111f8yC
- published: 18 Jul 2013
- views: 7894
In statistics, kernel density estimation (KDE) is a non-parametric way to estimate the probability density function of a random variable. Kernel density estimation is a fundamental data smoothing problem where inferences about the population are made, based on a finite data sample. In some fields such as signal processing and econometrics it is also termed the Parzen–Rosenblatt window method, after Emanuel Parzen and Murray Rosenblatt, who are usually credited with independently creating it in its current form.
This video is targeted to blind users.
Attribution:
Article text available under CC-BY-SA
Creative Commons image source in video
- published: 01 Oct 2014
- views: 5288
In probability and statistics, density estimation is the construction of an estimate, based on observed data, of an unobservable underlying probability density function. The unobservable density function is thought of as the density according to which a large population is distributed; the data are usually thought of as a random sample from that population.
A variety of approaches to density estimation are used, including Parzen windows and a range of data clustering techniques, including vector quantization. The most basic form of density estimation is a rescaled histogram.
We will consider records of the incidence of diabetes. The following is quoted verbatim from the data set description:
In this example, we construct three density estimates for "glu" (plasma glucose concentration), one conditional on the presence of diabetes, the second conditional on the absence of diabetes, and the third not conditional on diabetes. The conditional density estimates are then used to construct the probability of diabetes conditional on "glu".
This page contains text from Wikipedia, the Free Encyclopedia -
http://en.wikipedia.org/wiki/Density_estimation
This article is licensed under the Creative Commons Attribution-ShareAlike 3.0 Unported License, which means that you can copy and modify it as long as the entire work (including additions) remains under this license.
This article is licensed under the Creative Commons Attribution-ShareAlike 3.0 Unported License, which means that you can copy and modify it as long as the entire work (including additions) remains under this license.
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75:53Lecture 3: Density Estimation
Lecture 3: Density Estimation -
10:05Intro to Local (non-parametric) Density Estimation Methods, slecture
Intro to Local (non-parametric) Density Estimation Methods, slectureIntro to Local (non-parametric) Density Estimation Methods, slecture
This is a slecture for Prof. Boutin's course on Statistical Pattern Recognition (ECE662) made by Purdue student Nusaybah Amneh Abu-Mulaweh. The complete slecture is posted at https://www.projectrhea.org/rhea/index.php/Introduction_to_local_density_estimation_methods_ECE662_Spring2014_Nusaybah/Slecture_optimality_bayes_decision_rule_michaux_ECE662S14 To view other slectures on the same topic go to the ECE662 course wiki at https://www.projectrhea.org/rhea/index.php/2014_Spring_ECE_662_Boutin For more information about slectures, go to http://slectures.projectrhea.org -
28:13Slecture15 Non-parametric density estimation
Slecture15 Non-parametric density estimationSlecture15 Non-parametric density estimation
This is the introduction for non-parametric density estimation in ECE662. -
1:04Kernel Density Estimation (KDE) Part1 - Model Building and Validation
Kernel Density Estimation (KDE) Part1 - Model Building and ValidationKernel Density Estimation (KDE) Part1 - Model Building and Validation
This video is part of an online course, Model Building and Validation. Check out the course here: https://www.udacity.com/course/ud919. -
169:43Kernel Density Estimation and Kernel Regression
Kernel Density Estimation and Kernel RegressionKernel Density Estimation and Kernel Regression
Covers basic ideas of nonparametric estimation, kernel density estimation, kernel regression, uncertainty calculations in kernel regression models, and bandwidth selection. Uploaded by Justin Esarey, Rice University, on 1/22/2014. -
56:55Mod-01 Lec-11 Probability Density Estimation
Mod-01 Lec-11 Probability Density EstimationMod-01 Lec-11 Probability Density Estimation
Pattern Recognition and Application by Prof. P.K. Biswas,Department of Electronics & Communication Engineering,IIT Kharagpur.For more details on NPTEL visit http://nptel.ac.in -
9:18Point Pattern Analysis Part 5: Kernel Density Estimation
Point Pattern Analysis Part 5: Kernel Density EstimationPoint Pattern Analysis Part 5: Kernel Density Estimation
This presentation provides an introduction to kernel density estimation (KDE) techniques for visualizing event densities in maps. This video presentation was created as part of a peer-reviewed educational series on Spatial Analysis. The full suite of presentations can be accessed at http://ecolearnit.ifas.ufl.edu/. For more information on the statewide and online Geomatics program at the University of Florida, please visit us at http://flrec.ifas.ufl.edu/geomatics/ (Fort Lauderdale campus) or http://sfrc.ifas.ufl.edu/geomatics/ (Gainesville campus). -
48:269.1 Kernel Density Estimation | 9 Unsupervised Learning | Pattern Recognition Class 2012
9.1 Kernel Density Estimation | 9 Unsupervised Learning | Pattern Recognition Class 20129.1 Kernel Density Estimation | 9 Unsupervised Learning | Pattern Recognition Class 2012
The Pattern Recognition Class 2012 by Prof. Fred Hamprecht. It took place at the HCI / University of Heidelberg during the summer term of 2012. Website: http://hci.iwr.uni-heidelberg.de/MIP/Teaching/pr/ Playlist with all videos: http://goo.gl/gmOI6 Contents of this recording: density estimation outlier detection nonparametric classification nonparametric regression cluster analysis mode hunting average shifted histogram (ASH) kernel density estimation (KDE) Epanechnikov kernel box kernel b-spline kernel Syllabus: 1. Introduction 1.1 Applications of Pattern Recognition 1.2 k-Nearest Neighbors Classification 1.3 Probability Theory 1.4 Statistical Decision Theory 2. Correlation measures, Gaussian Models 2.1 Pearson Correlation 2.2 Alternative Correlation Measures 2.3 Gaussian Graphical Models 2.4 Discriminant Analysis 3. Dimensionality Reduction 3.1 Regularized LDA/QDA 3.2 Principal Component Analysis (PCA) 3.3 Bilinear Decompositions 4. Neural Networks 4.1 History of Neural Networks 4.2 Perceptrons 4.3 Multilayer Perceptrons 4.4 The Projection Trick 4.5 Radial Basis Function Networks 5. Support Vector Machines 5.1 Loss Functions 5.2 Linear Soft-Margin SVM 5.3 Nonlinear SVM 6. Kernels, Random Forest 6.1 Kernels 6.2 One-Class SVM 6.3 Random Forest 6.4 Random Forest Feature Importance 7. Regression 7.1 Least-Squares Regression 7.2 Optimum Experimental Design 7.3 Case Study: Functional MRI 7.4 Case Study: Computer Tomography 7.5 Regularized Regression 8. Gaussian Processes 8.1 Gaussian Process Regression 8.2 GP Regression: Interpretation 8.3 Gaussian Stochastic Processes 8.4 Covariance Function 9. Unsupervised Learning 9.1 Kernel Density Estimation 9.2 Cluster Analysis 9.3 Expectation Maximization 9.4 Gaussian Mixture Models 10. Directed Graphical Models 10.1 Bayesian Networks 10.2 Variable Elimination 10.3 Message Passing 10.4 State Space Models 11. Optimization 11.1 The Lagrangian Method 11.2 Constraint Qualifications 11.3 Linear Programming 11.4 The Simplex Algorithm 12. Structured Learning 12.1 structSVM 12.2 Cutting Planes -
3:07Kernel Density Estimation in Excel
Kernel Density Estimation in ExcelKernel Density Estimation in Excel
In this tutorial, we'll continue trying to infer the probability density function of a random variable, but we'll use another method, called kernel density estimation. Histogram video: http://www.youtube.com/watch?v=3wbDTuxq1Ac For more information on kernel density estimation, visit http://bit.ly/111f8yC -
16:07Kernel density estimation
Kernel density estimationKernel density estimation
In statistics, kernel density estimation (KDE) is a non-parametric way to estimate the probability density function of a random variable. Kernel density estimation is a fundamental data smoothing problem where inferences about the population are made, based on a finite data sample. In some fields such as signal processing and econometrics it is also termed the Parzen–Rosenblatt window method, after Emanuel Parzen and Murray Rosenblatt, who are usually credited with independently creating it in its current form. This video is targeted to blind users. Attribution: Article text available under CC-BY-SA Creative Commons image source in video
-
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Intro to Local (non-parametric) Density Estimation Methods, slecture
This is a slecture for Prof. Boutin's course on Statistical Pattern Recognition (ECE662) made by Purdue student Nusaybah Amneh Abu-Mulaweh. The complete slecture is posted at https://www.projectrhea.org/rhea/index.php/Introduction_to_local_density_estimation_methods_ECE662_Spring2014_Nusaybah/Slecture_optimality_bayes_decision_rule_michaux_ECE662S14 To view other slectures on the same topic go to the ECE662 course wiki at https://www.projectrhea.org/rhea/index.php/2014_Spring_ECE_662_Boutin For more information about slectures, go to http://slectures.projectrhea.org -
Slecture15 Non-parametric density estimation
This is the introduction for non-parametric density estimation in ECE662. -
Kernel Density Estimation (KDE) Part1 - Model Building and Validation
This video is part of an online course, Model Building and Validation. Check out the course here: https://www.udacity.com/course/ud919. -
Kernel Density Estimation and Kernel Regression
Covers basic ideas of nonparametric estimation, kernel density estimation, kernel regression, uncertainty calculations in kernel regression models, and bandwidth selection. Uploaded by Justin Esarey, Rice University, on 1/22/2014. -
Mod-01 Lec-11 Probability Density Estimation
Pattern Recognition and Application by Prof. P.K. Biswas,Department of Electronics & Communication Engineering,IIT Kharagpur.For more details on NPTEL visit http://nptel.ac.in -
Point Pattern Analysis Part 5: Kernel Density Estimation
This presentation provides an introduction to kernel density estimation (KDE) techniques for visualizing event densities in maps. This video presentation was created as part of a peer-reviewed educational series on Spatial Analysis. The full suite of presentations can be accessed at http://ecolearnit.ifas.ufl.edu/. For more information on the statewide and online Geomatics program at the University of Florida, please visit us at http://flrec.ifas.ufl.edu/geomatics/ (Fort Lauderdale campus) or http://sfrc.ifas.ufl.edu/geomatics/ (Gainesville campus). -
9.1 Kernel Density Estimation | 9 Unsupervised Learning | Pattern Recognition Class 2012
The Pattern Recognition Class 2012 by Prof. Fred Hamprecht. It took place at the HCI / University of Heidelberg during the summer term of 2012. Website: http://hci.iwr.uni-heidelberg.de/MIP/Teaching/pr/ Playlist with all videos: http://goo.gl/gmOI6 Contents of this recording: density estimation outlier detection nonparametric classification nonparametric regression cluster analysis mode hunting average shifted histogram (ASH) kernel density estimation (KDE) Epanechnikov kernel box kernel b-spline kernel Syllabus: 1. Introduction 1.1 Applications of Pattern Recognition 1.2 k-Nearest Neighbors Classification 1.3 Probability Theory 1.4 Statistical Decision Theory 2. Correlation measures, Gaussian Models 2.1 Pearson Correlation 2.2 Alternative Correlation Measures 2.3 Gaussian Graphical M... -
Kernel Density Estimation in Excel
In this tutorial, we'll continue trying to infer the probability density function of a random variable, but we'll use another method, called kernel density estimation. Histogram video: http://www.youtube.com/watch?v=3wbDTuxq1Ac For more information on kernel density estimation, visit http://bit.ly/111f8yC -
Kernel density estimation
In statistics, kernel density estimation (KDE) is a non-parametric way to estimate the probability density function of a random variable. Kernel density estimation is a fundamental data smoothing problem where inferences about the population are made, based on a finite data sample. In some fields such as signal processing and econometrics it is also termed the Parzen–Rosenblatt window method, after Emanuel Parzen and Murray Rosenblatt, who are usually credited with independently creating it in its current form. This video is targeted to blind users. Attribution: Article text available under CC-BY-SA Creative Commons image source in video
Lecture 3: Density Estimation
- Order: Reorder
- Duration: 75:53
- Updated: 27 Jan 2015
- views: 2290
Intro to Local (non-parametric) Density Estimation Methods, slecture
- Order: Reorder
- Duration: 10:05
- Updated: 05 May 2014
- views: 826
This is a slecture for Prof. Boutin's course on Statistical Pattern Recognition (ECE662) made by Purdue student Nusaybah Amneh Abu-Mulaweh. The complete slectur...
This is a slecture for Prof. Boutin's course on Statistical Pattern Recognition (ECE662) made by Purdue student Nusaybah Amneh Abu-Mulaweh. The complete slecture is posted at https://www.projectrhea.org/rhea/index.php/Introduction_to_local_density_estimation_methods_ECE662_Spring2014_Nusaybah/Slecture_optimality_bayes_decision_rule_michaux_ECE662S14
To view other slectures on the same topic go to the ECE662 course wiki at https://www.projectrhea.org/rhea/index.php/2014_Spring_ECE_662_Boutin
For more information about slectures, go to http://slectures.projectrhea.org
wn.com/Intro To Local (Non Parametric) Density Estimation Methods, Slecture
This is a slecture for Prof. Boutin's course on Statistical Pattern Recognition (ECE662) made by Purdue student Nusaybah Amneh Abu-Mulaweh. The complete slecture is posted at https://www.projectrhea.org/rhea/index.php/Introduction_to_local_density_estimation_methods_ECE662_Spring2014_Nusaybah/Slecture_optimality_bayes_decision_rule_michaux_ECE662S14
To view other slectures on the same topic go to the ECE662 course wiki at https://www.projectrhea.org/rhea/index.php/2014_Spring_ECE_662_Boutin
For more information about slectures, go to http://slectures.projectrhea.org
- published: 05 May 2014
- views: 826
Slecture15 Non-parametric density estimation
- Order: Reorder
- Duration: 28:13
- Updated: 01 May 2014
- views: 984
This is the introduction for non-parametric density estimation in ECE662.
This is the introduction for non-parametric density estimation in ECE662.
wn.com/Slecture15 Non Parametric Density Estimation
This is the introduction for non-parametric density estimation in ECE662.
- published: 01 May 2014
- views: 984
Kernel Density Estimation (KDE) Part1 - Model Building and Validation
- Order: Reorder
- Duration: 1:04
- Updated: 23 Feb 2015
- views: 1510
This video is part of an online course, Model Building and Validation. Check out the course here: https://www.udacity.com/course/ud919.
This video is part of an online course, Model Building and Validation. Check out the course here: https://www.udacity.com/course/ud919.
wn.com/Kernel Density Estimation (Kde) Part1 Model Building And Validation
This video is part of an online course, Model Building and Validation. Check out the course here: https://www.udacity.com/course/ud919.
- published: 23 Feb 2015
- views: 1510
Kernel Density Estimation and Kernel Regression
- Order: Reorder
- Duration: 169:43
- Updated: 23 Jan 2014
- views: 11100
Covers basic ideas of nonparametric estimation, kernel density estimation, kernel regression, uncertainty calculations in kernel regression models, and bandwidt...
Covers basic ideas of nonparametric estimation, kernel density estimation, kernel regression, uncertainty calculations in kernel regression models, and bandwidth selection. Uploaded by Justin Esarey, Rice University, on 1/22/2014.
wn.com/Kernel Density Estimation And Kernel Regression
Covers basic ideas of nonparametric estimation, kernel density estimation, kernel regression, uncertainty calculations in kernel regression models, and bandwidth selection. Uploaded by Justin Esarey, Rice University, on 1/22/2014.
- published: 23 Jan 2014
- views: 11100
Mod-01 Lec-11 Probability Density Estimation
- Order: Reorder
- Duration: 56:55
- Updated: 02 Jun 2014
- views: 3056
Pattern Recognition and Application by Prof. P.K. Biswas,Department of Electronics & Communication Engineering,IIT Kharagpur.For more details on NPTEL visit htt...
Pattern Recognition and Application by Prof. P.K. Biswas,Department of Electronics & Communication Engineering,IIT Kharagpur.For more details on NPTEL visit http://nptel.ac.in
wn.com/Mod 01 Lec 11 Probability Density Estimation
Pattern Recognition and Application by Prof. P.K. Biswas,Department of Electronics & Communication Engineering,IIT Kharagpur.For more details on NPTEL visit http://nptel.ac.in
- published: 02 Jun 2014
- views: 3056
Point Pattern Analysis Part 5: Kernel Density Estimation
- Order: Reorder
- Duration: 9:18
- Updated: 29 Jan 2016
- views: 357
This presentation provides an introduction to kernel density estimation (KDE) techniques for visualizing event densities in maps.
This video presentation was c...
This presentation provides an introduction to kernel density estimation (KDE) techniques for visualizing event densities in maps.
This video presentation was created as part of a peer-reviewed educational series on Spatial Analysis. The full suite of presentations can be accessed at http://ecolearnit.ifas.ufl.edu/. For more information on the statewide and online Geomatics program at the University of Florida, please visit us at http://flrec.ifas.ufl.edu/geomatics/ (Fort Lauderdale campus) or http://sfrc.ifas.ufl.edu/geomatics/ (Gainesville campus).
wn.com/Point Pattern Analysis Part 5 Kernel Density Estimation
This presentation provides an introduction to kernel density estimation (KDE) techniques for visualizing event densities in maps.
This video presentation was created as part of a peer-reviewed educational series on Spatial Analysis. The full suite of presentations can be accessed at http://ecolearnit.ifas.ufl.edu/. For more information on the statewide and online Geomatics program at the University of Florida, please visit us at http://flrec.ifas.ufl.edu/geomatics/ (Fort Lauderdale campus) or http://sfrc.ifas.ufl.edu/geomatics/ (Gainesville campus).
- published: 29 Jan 2016
- views: 357
9.1 Kernel Density Estimation | 9 Unsupervised Learning | Pattern Recognition Class 2012
- Order: Reorder
- Duration: 48:26
- Updated: 22 Nov 2012
- views: 12966
The Pattern Recognition Class 2012 by Prof. Fred Hamprecht. It took place at the HCI / University of Heidelberg during the summer term of 2012.
Website: http://...
The Pattern Recognition Class 2012 by Prof. Fred Hamprecht. It took place at the HCI / University of Heidelberg during the summer term of 2012.
Website: http://hci.iwr.uni-heidelberg.de/MIP/Teaching/pr/
Playlist with all videos: http://goo.gl/gmOI6
Contents of this recording:
density estimation
outlier detection
nonparametric classification
nonparametric regression
cluster analysis
mode hunting
average shifted histogram (ASH)
kernel density estimation (KDE)
Epanechnikov kernel
box kernel
b-spline kernel
Syllabus:
1. Introduction
1.1 Applications of Pattern Recognition
1.2 k-Nearest Neighbors Classification
1.3 Probability Theory
1.4 Statistical Decision Theory
2. Correlation measures, Gaussian Models
2.1 Pearson Correlation
2.2 Alternative Correlation Measures
2.3 Gaussian Graphical Models
2.4 Discriminant Analysis
3. Dimensionality Reduction
3.1 Regularized LDA/QDA
3.2 Principal Component Analysis (PCA)
3.3 Bilinear Decompositions
4. Neural Networks
4.1 History of Neural Networks
4.2 Perceptrons
4.3 Multilayer Perceptrons
4.4 The Projection Trick
4.5 Radial Basis Function Networks
5. Support Vector Machines
5.1 Loss Functions
5.2 Linear Soft-Margin SVM
5.3 Nonlinear SVM
6. Kernels, Random Forest
6.1 Kernels
6.2 One-Class SVM
6.3 Random Forest
6.4 Random Forest Feature Importance
7. Regression
7.1 Least-Squares Regression
7.2 Optimum Experimental Design
7.3 Case Study: Functional MRI
7.4 Case Study: Computer Tomography
7.5 Regularized Regression
8. Gaussian Processes
8.1 Gaussian Process Regression
8.2 GP Regression: Interpretation
8.3 Gaussian Stochastic Processes
8.4 Covariance Function
9. Unsupervised Learning
9.1 Kernel Density Estimation
9.2 Cluster Analysis
9.3 Expectation Maximization
9.4 Gaussian Mixture Models
10. Directed Graphical Models
10.1 Bayesian Networks
10.2 Variable Elimination
10.3 Message Passing
10.4 State Space Models
11. Optimization
11.1 The Lagrangian Method
11.2 Constraint Qualifications
11.3 Linear Programming
11.4 The Simplex Algorithm
12. Structured Learning
12.1 structSVM
12.2 Cutting Planes
wn.com/9.1 Kernel Density Estimation | 9 Unsupervised Learning | Pattern Recognition Class 2012
The Pattern Recognition Class 2012 by Prof. Fred Hamprecht. It took place at the HCI / University of Heidelberg during the summer term of 2012.
Website: http://hci.iwr.uni-heidelberg.de/MIP/Teaching/pr/
Playlist with all videos: http://goo.gl/gmOI6
Contents of this recording:
density estimation
outlier detection
nonparametric classification
nonparametric regression
cluster analysis
mode hunting
average shifted histogram (ASH)
kernel density estimation (KDE)
Epanechnikov kernel
box kernel
b-spline kernel
Syllabus:
1. Introduction
1.1 Applications of Pattern Recognition
1.2 k-Nearest Neighbors Classification
1.3 Probability Theory
1.4 Statistical Decision Theory
2. Correlation measures, Gaussian Models
2.1 Pearson Correlation
2.2 Alternative Correlation Measures
2.3 Gaussian Graphical Models
2.4 Discriminant Analysis
3. Dimensionality Reduction
3.1 Regularized LDA/QDA
3.2 Principal Component Analysis (PCA)
3.3 Bilinear Decompositions
4. Neural Networks
4.1 History of Neural Networks
4.2 Perceptrons
4.3 Multilayer Perceptrons
4.4 The Projection Trick
4.5 Radial Basis Function Networks
5. Support Vector Machines
5.1 Loss Functions
5.2 Linear Soft-Margin SVM
5.3 Nonlinear SVM
6. Kernels, Random Forest
6.1 Kernels
6.2 One-Class SVM
6.3 Random Forest
6.4 Random Forest Feature Importance
7. Regression
7.1 Least-Squares Regression
7.2 Optimum Experimental Design
7.3 Case Study: Functional MRI
7.4 Case Study: Computer Tomography
7.5 Regularized Regression
8. Gaussian Processes
8.1 Gaussian Process Regression
8.2 GP Regression: Interpretation
8.3 Gaussian Stochastic Processes
8.4 Covariance Function
9. Unsupervised Learning
9.1 Kernel Density Estimation
9.2 Cluster Analysis
9.3 Expectation Maximization
9.4 Gaussian Mixture Models
10. Directed Graphical Models
10.1 Bayesian Networks
10.2 Variable Elimination
10.3 Message Passing
10.4 State Space Models
11. Optimization
11.1 The Lagrangian Method
11.2 Constraint Qualifications
11.3 Linear Programming
11.4 The Simplex Algorithm
12. Structured Learning
12.1 structSVM
12.2 Cutting Planes
- published: 22 Nov 2012
- views: 12966
Kernel Density Estimation in Excel
- Order: Reorder
- Duration: 3:07
- Updated: 18 Jul 2013
- views: 7894
In this tutorial, we'll continue trying to infer the probability density function of a random variable, but we'll use another method, called kernel density esti...
In this tutorial, we'll continue trying to infer the probability density function of a random variable, but we'll use another method, called kernel density estimation.
Histogram video: http://www.youtube.com/watch?v=3wbDTuxq1Ac
For more information on kernel density estimation, visit http://bit.ly/111f8yC
wn.com/Kernel Density Estimation In Excel
In this tutorial, we'll continue trying to infer the probability density function of a random variable, but we'll use another method, called kernel density estimation.
Histogram video: http://www.youtube.com/watch?v=3wbDTuxq1Ac
For more information on kernel density estimation, visit http://bit.ly/111f8yC
- published: 18 Jul 2013
- views: 7894
Kernel density estimation
- Order: Reorder
- Duration: 16:07
- Updated: 01 Oct 2014
- views: 5288
In statistics, kernel density estimation (KDE) is a non-parametric way to estimate the probability density function of a random variable. Kernel density estimat...
In statistics, kernel density estimation (KDE) is a non-parametric way to estimate the probability density function of a random variable. Kernel density estimation is a fundamental data smoothing problem where inferences about the population are made, based on a finite data sample. In some fields such as signal processing and econometrics it is also termed the Parzen–Rosenblatt window method, after Emanuel Parzen and Murray Rosenblatt, who are usually credited with independently creating it in its current form.
This video is targeted to blind users.
Attribution:
Article text available under CC-BY-SA
Creative Commons image source in video
wn.com/Kernel Density Estimation
In statistics, kernel density estimation (KDE) is a non-parametric way to estimate the probability density function of a random variable. Kernel density estimation is a fundamental data smoothing problem where inferences about the population are made, based on a finite data sample. In some fields such as signal processing and econometrics it is also termed the Parzen–Rosenblatt window method, after Emanuel Parzen and Murray Rosenblatt, who are usually credited with independently creating it in its current form.
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- published: 01 Oct 2014
- views: 5288
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75:53
Lecture 3: Density Estimation
Lecture Date: 01/21/2015
published: 27 Jan 2015
Lecture 3: Density Estimation
Lecture 3: Density Estimation
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- published: 27 Jan 2015
- views: 2290
10:05
Intro to Local (non-parametric) Density Estimation Methods, slecture
This is a slecture for Prof. Boutin's course on Statistical Pattern Recognition (ECE662) m...
published: 05 May 2014
Intro to Local (non-parametric) Density Estimation Methods, slecture
Intro to Local (non-parametric) Density Estimation Methods, slecture
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- published: 05 May 2014
- views: 826
28:13
Slecture15 Non-parametric density estimation
This is the introduction for non-parametric density estimation in ECE662.
published: 01 May 2014
Slecture15 Non-parametric density estimation
Slecture15 Non-parametric density estimation
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- published: 01 May 2014
- views: 984
1:04
Kernel Density Estimation (KDE) Part1 - Model Building and Validation
This video is part of an online course, Model Building and Validation. Check out the cours...
published: 23 Feb 2015
Kernel Density Estimation (KDE) Part1 - Model Building and Validation
Kernel Density Estimation (KDE) Part1 - Model Building and Validation
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- published: 23 Feb 2015
- views: 1510
169:43
Kernel Density Estimation and Kernel Regression
Covers basic ideas of nonparametric estimation, kernel density estimation, kernel regressi...
published: 23 Jan 2014
Kernel Density Estimation and Kernel Regression
Kernel Density Estimation and Kernel Regression
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- published: 23 Jan 2014
- views: 11100
56:55
Mod-01 Lec-11 Probability Density Estimation
Pattern Recognition and Application by Prof. P.K. Biswas,Department of Electronics & Commu...
published: 02 Jun 2014
Mod-01 Lec-11 Probability Density Estimation
Mod-01 Lec-11 Probability Density Estimation
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- published: 02 Jun 2014
- views: 3056
9:18
Point Pattern Analysis Part 5: Kernel Density Estimation
This presentation provides an introduction to kernel density estimation (KDE) techniques f...
published: 29 Jan 2016
Point Pattern Analysis Part 5: Kernel Density Estimation
Point Pattern Analysis Part 5: Kernel Density Estimation
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- published: 29 Jan 2016
- views: 357
48:26
9.1 Kernel Density Estimation | 9 Unsupervised Learning | Pattern Recognition Class 2012
The Pattern Recognition Class 2012 by Prof. Fred Hamprecht. It took place at the HCI / Uni...
published: 22 Nov 2012
9.1 Kernel Density Estimation | 9 Unsupervised Learning | Pattern Recognition Class 2012
9.1 Kernel Density Estimation | 9 Unsupervised Learning | Pattern Recognition Class 2012
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- published: 22 Nov 2012
- views: 12966
3:07
Kernel Density Estimation in Excel
In this tutorial, we'll continue trying to infer the probability density function of a ran...
published: 18 Jul 2013
Kernel Density Estimation in Excel
Kernel Density Estimation in Excel
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- published: 18 Jul 2013
- views: 7894
16:07
Kernel density estimation
In statistics, kernel density estimation (KDE) is a non-parametric way to estimate the pro...
published: 01 Oct 2014
Kernel density estimation
Kernel density estimation
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- published: 01 Oct 2014
- views: 5288
Lecture 3: Density Estimation...
Intro to Local (non-parametric) Density Estimation...
Slecture15 Non-parametric density estimation...
Kernel Density Estimation (KDE) Part1 - Model Buil...
Kernel Density Estimation and Kernel Regression...
Mod-01 Lec-11 Probability Density Estimation...
Point Pattern Analysis Part 5: Kernel Density Esti...
9.1 Kernel Density Estimation | 9 Unsupervised Lea...
Kernel Density Estimation in Excel...
Kernel density estimation...
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Teacher had sex with 13-year-old student, got pregnant, authorities say
Edit Springfield News-Sun 02 Jun 2016
A Texas teacher accused of having sex with a 13-year-old student turned herself in to police Wednesday. According to KTRK, Alexandria Vera, 24, of Houston is accused of sexually abusing the boy, who was an eighth-grade student in her English class at Stovall Middle School last summer ... Read more trending stories ... Read more here ... ....
photo: AP Photo/Chiang Ying-ying
ASUS' $599 home robot is smarter than it looks
Edit Engadget 02 Jun 2016photo: AP / Bebeto Matthews
Protesters punch, throw eggs at Trump supporters in San Jose
Edit Atlanta Journal 03 Jun 2016
A group of protesters attacked Donald Trump supporters who were leaving the candidate's rally in San Jose on Thursday night. A dozen or more people were punched, at least one person was pelted with an egg and Trump hats grabbed from supporters were set on fire on the ground. There were no immediate reports of injuries or arrests, but police had declared the gathering an unlawful assembly about a half-hour after the rally ended ... Hong) ... ....
photo: Creative Commons / Bruno de Giusti
Fish prefer to eat plastic over food – and it is killing them, study suggests
Edit The Independent 03 Jun 2016
Microplastic particles appear to be killing fish because their larvae prefer to eat them rather than their actual food, researchers have warned ... Read more ....
photo: AP / Sayyid Abdul Azim
'Armenian genocide' vote: Turkey warns Germany
Edit Al Jazeera 03 Jun 2016
Turkish President Recep Tayyip Erdogan has said bilateral relations between Germany and Turkey will be "seriously affected", after the German parliament labelled as "genocide" the 1915 killings of Armenians by Ottoman Empire ... Turkey agrees that many Armenians died in ethnic fighting and the deportation process between 1915 and 1917 during World War I, putting its estimate at 300,000 casualties ... OPINION. Remembering the Armenians ... Source....
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Rep. Costa, Federal and State Agencies Work Together to Bolster Efforts to Suppress Wildfires in California (Jim Costa)
Edit Public Technologies 03 Jun 2016
(Source. Jim Costa). Fresno, CA - Today, Rep ... In addition to Rep ... Costa ... According to the United States Forest Service, since 2010, an estimated 40 million trees have died in California's national forests due to the unprecedented and ongoing drought, bark beetle infestation and high tree densities. An estimated 29 million trees died in 2015 alone, which was the driest of the four years of drought currently affecting California. Rep ... ###....
Attend a Graduate Defense (Boise State University)
Edit Public Technologies 02 Jun 2016
(Source. Boise State University). The campus community is invited to attend the following graduate defenses.. Erica Morrill. When. 9 p.m ... Where ... I evaluated temporal and spatial changes in hotspots between survey periods using point density estimation and KDE+. Additionally, of the 120 randomly selected sites, I calculated which fell within an area delineated as a hotspot and which did not as defined by the point density estimation analysis....
Tinka Increases Inferred Mineral Resources at Ayawilca; Zinc Resource Now 18.8 Million Tonnes at 8.2% ...
Edit Stockhouse 25 May 2016
... grades being low it is assumed that lead and silver will be recovered in a lead concentrate. Density was estimated to be 3.6 t/m3 for the Zinc Zones and 3.9 t/m3 for the Tin Zones. All Mineral Resources were assigned to the Inferred category due to the widely spaced drilling. No Mineral Reserves have yet been estimated at Ayawilca....
Tinka Increases Inferred Mineral Resources At Ayawilca; Zinc Resource Now 18.8 Million Tonnes At 8.2% Zinc Eq, Initial Tin Resource 5.4 Million Tonnes At 0.89% Tin Eq (Tinka Resources Limited)
Edit Public Technologies 25 May 2016
Both of the Mineral Resources are in accordance with the NI 43-101 Inferred Mineral Resource category at a cut-off grade equivalent to approximately US$60/t, as estimated by Roscoe Postle Associates Inc ... In addition, we are very pleased to report a tin-copper-silver Mineral Resource estimate for the first time ... Detail of Mineral Resource Estimates ... Density was estimated to be 3.6 t/m for the Zinc Zones and 3.9 t/m for the Tin Zones....
Coal Resource Estimate at Dartbrook of 1.2 Billion Tonnes (Australian Pacific Coal Limited)
Edit Public Technologies 24 May 2016
AUSTRALIAN PACIFIC COAL COMMISSIONED COAL RESOURCE ESTIMATE AT DARTBROOK OF 1.2 BILLION TONNES ... Report commissioned by Australian Pacific Coal Ltd ahead of completion of the acquisition identifies a total Coal Resource Estimate of 1.2 billion tonnes at Dartbrook (100% Project ownership basis) ... The CRE was estimated by JB Mining ... Estimation ... Bulk density In-situ density is estimated using the Preston & Sanders formula....
In a first, volunteers to use point count method for machan census
Edit The Times of India 19 May 2016
The distance of the animals from the observers need not be measured precisely but can be group in classes of distances, say 0-10m; 10-20m; 20-30m etc and the result so generated does not decrease the efficiency of the density estimate procedure," said WII scientist Bilal Habib ... "The density of wild animals of each machan site shall be taken into account and calculated the average density....
Mercy Doctor Discusses New Law For Women With Dense Breasts (Sisters of Mercy Health System)
Edit Public Technologies 04 May 2016
(Source. Sisters of Mercy Health System). Dr. Alan Hollingsworth, M.D ... Dr ... Since as early as 1993, Dr. Hollingsworth has championed for breast MRI and additional screening by developing a breast density scoring system for Mercy patients with higher density. He has also estimated that breast cancer may not have been diagnosed in as many as half of his patients had he relied on mammography alone. Dr ... (noodl. 33397076) ....
Americas Silver Corporation Announces San Rafael Pre-Feasibility Study, Updates to Resources and Reserves, and Fourth ...
Edit Stockhouse 31 Mar 2016
Varying cut-off grades have been used depending on the mine, methods of extraction and type of ore contained in the reserves. Mineral resource metal grades and material densities have been estimated using industry-standard methods appropriate for each mineral project with support of various commercially available mining software ......
How to use the new Pareto, Histogram, and Waterfall formats
Edit PC World 23 Mar 2016
Excel 2016’s many new features include six new chart types. We’ll go over Histogram, Pareto, and Waterfall and talk about how they could be used with your data. We covered Treemap, Sunburst, and Box & Whisker earlier.Histogram shows ranges of values ... Histogram charts are for exploring and analyzing the frequencies within a distribution, statistics, density estimation, etc ... ....
Georgia Southern Examines Improved Nonparametric Estimations (ASPH - Association of Schools of Public Health)
Edit Public Technologies 17 Mar 2016
However, the cut-off point is usually unknown and needs to be estimated by some optimization criteria ... Therefore, it is desirable to estimate the optimal cut-off point associated with the Youden index ... In this study, kernel density estimation is utilized to numerically solve for an estimate of the optimal cut-off point ... http.//www.aspph.org/georgia-southern-examines-improved-nonparametric-estimations/....
The Most Fascinating Work Facebook Is Doing In Machine Learning
Edit Huffington Post 15 Mar 2016
What are the most interesting things Facebook is doing in ML research? originally appeared on Quora - the knowledge sharing network where compelling questions are answered by people with unique insights ... We recently shared how the features from these networks are used to generate population density maps using satellite imagery (check out this cool video). (this is the cover image for the video on population density estimation) ... ....
New Maps May Help Reduce Threats to Whales, Dolphins in US Waters (Duke University)
Edit Public Technologies 04 Mar 2016
... were able to create maps that display estimates of species' population densities, region by region, on a monthly or year-round basis, and also track the seasonal movements of 11 species ... 'Insights provided by these new maps can be very influential in shaping the outcomes of these decisions because population density estimates are used by U.S....
Georgia Southern Examines a Test of Symmetry (ASPH - Association of Schools of Public Health)
Edit Public Technologies 04 Mar 2016
(Source. ASPH - Association of Schools of Public Health). Many statistical applications and inferences rely on the validity of the underlying distributional assumption ... This paper provides a test of symmetry using kernel density estimation and the Kullback-Leibler information ... Dr ... Original Document ... (noodl. 32437316) ....
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