View uci-20070111 ailerons (public)

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Summary

(No information yet)

License
unknown (from Weka repository)
Dependencies
Tags
arff slurped Weka
Attribute Types
Integer,Floating Point
Download
# Instances: 13750 / # Attributes: 41
HDF5 (4.1 MB) XML CSV ARFF LibSVM Matlab Octave

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Original Data Format
arff
Name
ailerons
Version mldata
0
Comment

This data set addresses a control problem, namely flying a F16 aircraft. The attributes describe the status of the aeroplane, while the goal is to predict the control action on the ailerons of the aircraft.

Characteristics: (7154+6596) cases, 41 continuous attributes Source: collection of regression datasets by Luis Torgo (ltorgo@ncc.up.pt) at http://www.ncc.up.pt/~ltorgo/Regression/DataSets.html Original source: Experiments of Rui Camacho (rcamacho@garfield.fe.up.pt)

Names
climbRate,Sgz,p,q,curPitch,curRoll,absRoll,diffClb,diffRollRate,diffDiffClb,
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)
    clim... Sgz p q curP... curR... absR... diff... diff... diff... ...
    2 -56 -0.33 -0.09 0.9 0.2 -11 12 0.004 -0.1 ...
    470 -39 0.02 0.12 0.39 -0.6 -12 8 0.009 -1.6 ...
    165 4 0.14 0.14 0.78 0.4 -11 -9 -0.003 -0.2 ...
    -113 5 -0.12 0.11 1.06 0.6 -10 -7 -0.008 0.0 ...
    -411 -21 -0.17 0.07 1.33 -0.6 -11 0 0.002 0.1 ...
    -105 -42 0.23 -0.06 0.92 -0.6 -12 10 0.011 0.0 ...
    144 -40 0.31 -0.01 0.67 0.6 -10 5 -0.005 -0.2 ...
    249 -11 -0.38 0.06 0.56 0.4 -11 -2 -0.008 -0.1 ...
    229 -4 -0.45 0.05 0.58 -0.4 -11 -2 0.002 0.3 ...
    -60 14 0.22 0.15 0.89 -1.6 -13 -8 0.01 -0.2 ...
    ... ... ... ... ... ... ... ... ... ... ...
Description

A gzip'ed tar containing UCI and UCI KDD datasets (uci-20070111.tar.gz, 17,952,832 Bytes)

URLs
(No information yet)
Publications
    Data Source
    http://www.ics.uci.edu/~mlearn/MLRepository.html http://kdd.ics.uci.edu/
    Measurement Details
    Usage Scenario
    revision 1
    by mldata on 2011-09-14 15:25

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    Acknowledgements

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