View uci-20070111 delta_elevators (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
Integer,Floating Point
Download
# Instances: 9517 / # Attributes: 7
HDF5 (494.9 KB) XML CSV ARFF LibSVM Matlab Octave

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Original Data Format
arff
Name
delta_elevators
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, 7 continuous attributes

Names
climbRate,Altitude,RollRate,curRoll,diffClb,diffDiffClb,Se,
Types
  1. numeric
  2. numeric
  3. numeric
  4. numeric
  5. numeric
  6. numeric
  7. numeric
Data (first 10 data points)
    clim... Alti... Roll... curR... diff... diff... Se
    2.0 -50.0 -0.00... -0.001 0.2 0.0 -0.001
    6.5 -40.0 -0.001 -0.009 0.2 0.0 0.003
    -5.9 -10.0 -0.00... -0.004 -0.1 0.0 -0.001
    -6.2 -30.0 -0.00... -0.011 0.1 0.0 -0.002
    -0.2 -40.0 0.0059 -0.005 0.1 0.0 0.001
    -2.0 10.0 0.0051 -0.023 -0.1 0.0 -0.001
    -5.6 0.0 0.0046 0.0 -0.1 0.0 -0.002
    -3.9 -30.0 -0.00... 0.005 0.0 0.0 -0.001
    -1.7 -60.0 0.0053 0.013 -0.1 0.0 0.001
    -2.9 -60.0 0.0024 0.019 -0.1 0.0 -0.001
    ... ... ... ... ... ... ...
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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