View Friedman-datasets fri_c0_1000_10 (public)

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Summary

(No information yet)

License
unknown (from Weka repository)
Dependencies
Tags
arff slurped Weka
Attribute Types
Floating Point
Download
# Instances: 1000 / # Attributes: 11
HDF5 (96.5 KB) XML CSV ARFF LibSVM Matlab Octave

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Original Data Format
arff
Name
fri_c0_1000_10
Version mldata
0
Comment
Names
oz1,oz2,oz3,oz4,oz5,oz6,oz7,oz8,oz9,oz10,
Types
  1. numeric
  2. numeric
  3. numeric
  4. numeric
  5. numeric
  6. numeric
  7. numeric
  8. numeric
  9. numeric
  10. numeric
Data (first 10 data points)
    oz1 oz2 oz3 oz4 oz5 oz6 oz7 oz8 oz9 oz10 ...
    0.54... 1.44... -0.67... -1.07... 0.59... -1.34... -0.97... 0.27... -0.22... -0.05... ...
    0.06... 1.43... -0.62... 1.47... 1.65... 1.08... 0.87... -1.60... 1.47... -1.12... ...
    -0.17... -1.46... 1.08... 1.13... -0.55... 0.64... 1.58... -0.72... -0.17... 0.48... ...
    -1.38... -0.86... -0.20... 1.14... 0.32... -1.59... 0.72... -1.33... -0.44... -0.19... ...
    0.19... -0.41... -1.61... -0.37... 0.26... -0.93... -0.05... 0.52... 0.50... -0.91... ...
    1.69... 0.49... -1.56... -0.87... 0.34... -0.17... -0.42... -1.26... -1.11... -1.10... ...
    1.55... 0.53... 1.41... -0.97... -1.68... -0.54... 1.44... 1.63... 0.96... 0.60... ...
    1.14... 0.01... 1.55... 0.45... 1.57... 0.36... -0.39... 1.44... -0.36... 1.30... ...
    -1.76... -0.56... -0.32... -0.30... -1.58... 1.50... 0.28... -0.00... -1.46... -1.28... ...
    1.21... 0.28... -1.70... 0.85... 0.01... -0.23... -0.87... -0.98... 0.18... -1.68... ...
    ... ... ... ... ... ... ... ... ... ... ...
Description

A zip file containing 80 artificial datasets generated from the Friedman function donated by Dr. M. Fatih Amasyali (Yildiz Technical Unversity) (Friedman-datasets.zip, 5,802,204 Bytes)

URLs
(No information yet)
Publications
    Data Source
    http://www.ce.yildiz.edu.tr/en/myindex.php?id=14 http://www.ce.yildiz.edu.tr/
    Measurement Details
    Usage Scenario
    revision 1
    by mldata on 2010-11-06 10:01

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    Acknowledgements

    This project is supported by PASCAL (Pattern Analysis, Statistical Modelling and Computational Learning)
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