View uci-20070111 delta_ailerons (public)

2010-11-06 09:59 by mldata | Version 1 | Rating Empty StarEmpty StarEmpty StarEmpty StarEmpty StarEmpty Star
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Summary

(No information yet)

License
unknown (from Weka repository)
Dependencies
Tags
arff slurped Weka
Attribute Types
Floating Point
Download
# Instances: 7129 / # Attributes: 6
HDF5 (345.3 KB) XML CSV ARFF LibSVM Matlab Octave

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Original Data Format
arff
Name
delta_ailerons
Version mldata
0
Comment

This data set is also obtained from the task of controlling the ailerons of a F16 aircraft, although the target variable and attributes are different from the ailerons domain. The target variable here is a variation instead of an absolute value, and there was some pre-selection of the 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). Characteristics: 7129 cases, 6 continuous attributes

Names
RollRate,PitchRate,currPitch,currRoll,diffRollRate,Sa,
Types
  1. numeric
  2. numeric
  3. numeric
  4. numeric
  5. numeric
  6. numeric
Data (first 10 data points)
    Roll... Pitc... curr... curr... diff... Sa
    0.002 0.0025 0.015 0.01 -4e-05 -0.00...
    -0.00... 0.0003 0.022 -0.011 5e-05 0.0003
    0.0059 -0.00... 0.014 -0.005 0.00... -0.00...
    0.0046 0.0001 0.011 0.015 -9e-05 -0.00...
    -0.00... 0.0012 0.01 0.001 -0.00... 0.0002
    -0.00... 0.0013 0.01 -0.013 6e-05 0.0001
    0.0054 0.0027 0.017 -0.022 0.00... -0.00...
    0.0061 0.0018 0.019 -0.008 -1e-05 -0.00...
    -0.00... 0.0008 0.015 -0.014 1e-05 0.0001
    0.0041 0.0012 0.013 -0.011 5e-05 0.0001
    ... ... ... ... ... ...
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 2010-11-06 09:59

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    Acknowledgements

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