Changes between Version 25 and Version 26 of UsersSamples

Timestamp:

03/04/11 09:20:00 (14 years ago)

Author:

gkronber

Comment:

added more screenshots (for GP samples)

UsersSamples

@@ -223 +223 @@
 === Genetic programming - Symbolic Regression (Boston Housing)===
 [export:/misc/samples/GP_Regression-Boston-Housing.hl GP_Regression-Boston-Housing.hl]
+[[Image(GP_Boston-Housing-screenshot.png,  width=500, right, margin-right=30, margin-left=30)]]
 
 Example for a simple genetic programming algorithm to create a regression model for the estimation of the median value of houses in a certain in the Boston area based on other parameters of that region. The original dataset was downloaded from http://archive.ics.uci.edu/ml/datasets/Housing. 
@@ -251 +252 @@
 === Genetic programming - Symbolic Regression (Tower)===
 [export:/misc/samples/GP_Regression-TowerResponse.hl GP_Regression-TowerResponse.hl]
+[[Image(GP_TowerResponse-screenshot.png,  width=500, right, margin-right=30, margin-left=30)]]
 
 Example for a simple genetic programming algorithm to create a regression model for the estimation of a product quality parameter in an industrial chemical process. The original dataset was downloaded from http://vanillamodeling.com/realproblems.html.
@@ -267 +269 @@
 === Genetic programming - Symbolic Regression (Mackey-Glass)===
 [export:/misc/samples/GP_Regression-Mackey-Glass.hl GP_Regression-Mackey-Glass.hl]
+[[Image(GP_Mackey-Glass-screenshot.png,  width=500, right, margin-right=30, margin-left=30)]]
 
 Example for a simple genetic programming algorithm to create a dynamic model for the one-step prediction of the chaotic Mackey Glass (T=17) time series. The original dataset was downloaded from http://www.bme.ogi.edu/~ericwan/data.html.
@@ -284 +287 @@
 === Genetic programming - Symbolic Classification (Wisconsin)===
 [export:/misc/samples/GP_Classification-WisconsinDiagnostic.hl GP_Classification-WisconsinDiagnostic.hl]
+[[Image(GP_WisconsinDiagnostic-screenshot.png,  width=500, right, margin-right=30, margin-left=30)]]
 
 Example for a simple genetic programming algorithm to create a classification model for the estimation of malignant or benign tumor diagnosis based on features extracted through analysis of tumorous cells in a tissue sample. The original dataset was downloaded from the UCI Machine Learning Repository (http://archive.ics.uci.edu/ml/datasets/Breast+Cancer+Wisconsin+(Diagnostic)). The algorithm uses all available GP manipulation operators, single-point cross-over and squared Pearson's correlation coefficient evaluation.
@@ -289 +293 @@
 The algorithm reaches an accuracy of ~95% on the test set.
 This algorithm is supposed to be a demo for GP in HeuristicLab, the aim is not to find high quality models.
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@@ -294 +315 @@
 === Genetic programming - Symbolic Classification (Mammography)===
 [export:/misc/samples/GP_Classification-Mammography.hl GP_Classification-Mammography.hl]
+[[Image(GP_Mammography-screenshot.png,  width=500, right, margin-right=30, margin-left=30)]]
 
  A genetic programming algorithm to create a classification model for the prediction of malignant or benign tumor diagnosis based on features extracted through a non-invasive mammography breast cancer screening. Original dataset stems from the UCI Machine Learning Repository (http://archive.ics.uci.edu/ml/datasets/Mammographic+Mass).