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Changes between Version 25 and Version 26 of UsersSamples


Ignore:
Timestamp:
03/04/11 09:20:00 (12 years ago)
Author:
gkronber
Comment:

added more screenshots (for GP samples)

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  • UsersSamples

    v25 v26  
    223223=== Genetic programming - Symbolic Regression (Boston Housing)===
    224224[export:/misc/samples/GP_Regression-Boston-Housing.hl GP_Regression-Boston-Housing.hl]
     225[[Image(GP_Boston-Housing-screenshot.png,  width=500, right, margin-right=30, margin-left=30)]]
    225226
    226227Example 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.
     
    251252=== Genetic programming - Symbolic Regression (Tower)===
    252253[export:/misc/samples/GP_Regression-TowerResponse.hl GP_Regression-TowerResponse.hl]
     254[[Image(GP_TowerResponse-screenshot.png,  width=500, right, margin-right=30, margin-left=30)]]
    253255
    254256Example 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.
     
    267269=== Genetic programming - Symbolic Regression (Mackey-Glass)===
    268270[export:/misc/samples/GP_Regression-Mackey-Glass.hl GP_Regression-Mackey-Glass.hl]
     271[[Image(GP_Mackey-Glass-screenshot.png,  width=500, right, margin-right=30, margin-left=30)]]
    269272
    270273Example 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.
     
    284287=== Genetic programming - Symbolic Classification (Wisconsin)===
    285288[export:/misc/samples/GP_Classification-WisconsinDiagnostic.hl GP_Classification-WisconsinDiagnostic.hl]
     289[[Image(GP_WisconsinDiagnostic-screenshot.png,  width=500, right, margin-right=30, margin-left=30)]]
    286290
    287291Example 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.
     
    289293The algorithm reaches an accuracy of ~95% on the test set.
    290294This algorithm is supposed to be a demo for GP in HeuristicLab, the aim is not to find high quality models.
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    294315=== Genetic programming - Symbolic Classification (Mammography)===
    295316[export:/misc/samples/GP_Classification-Mammography.hl GP_Classification-Mammography.hl]
     317[[Image(GP_Mammography-screenshot.png,  width=500, right, margin-right=30, margin-left=30)]]
    296318
    297319 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).