View SensIT Vehicle (seismic)_scale (public)

2010-11-01 11:48 by mldata | Version 1 | Rating Empty StarEmpty StarEmpty StarEmpty StarEmpty StarEmpty Star
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Summary

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unknown (from LibSVMTools repository)
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libsvm LibSVMTools slurped
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# Instances: 98528 / # Attributes: 51
HDF5 (38.3 MB) XML CSV ARFF LibSVM Matlab Octave

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Original Data Format
libsvm
Name
sensit-vehicle-seismic_scale
Version mldata
0
Comment

LibSVM

Names
Data (first 10 data points)
    2 -0.97... 0.75... -0.84... -0.94... -0.98... -0.99... -0.99... -0.99... -0.99... ...
    2 -0.80... -0.66... -0.41... -0.24... -0.94... -0.96... -0.96... -0.99... -0.97... ...
    2 -0.20... -0.93... -0.22... -0.84... -0.87... -0.95... -0.97... -0.97... -0.96... ...
    2 0.81... -0.96... -0.93... -0.95... -0.95... -0.96... -0.95... -0.96... -0.96... ...
    2 0.30... -0.83... -0.70... -0.93... -0.96... -0.97... -0.96... -0.96... -0.95... ...
    1 -0.95... -0.94... -0.99... -0.12... -0.18... -0.92... -0.96... -0.95... -0.98... ...
    1 -0.99... -0.94... -0.65... -0.11... -0.39... -0.98... -0.89... -0.99... -0.99... ...
    3 -0.42... -0.56... -0.15... -0.98... -0.96... -0.98... -0.98... -0.98... -0.96... ...
    3 0.48... -0.86... -0.92... -0.97... -0.96... -0.96... -0.96... -0.96... -0.96... ...
    1 -0.99... -0.99... -0.97... 0.64... -0.71... -0.97... -0.96... -0.98... -0.99... ...
    ... ... ... ... ... ... ... ... ... ... ...
Description

Preprocessing: Regenerate features by the authors' matlab scripts (see Sec. C of Appendix A), then randomly select 10% instances from the noise class so that the class proportion is 1:1:2 (AAV:DW:noise). The training/testing sets are from a random 80% and 20% split of the data. [MD04a] # of classes: 3# of data: 78,823 / 19,705 (testing) # of features: 50

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Publications
    Data Source
    http://www.ece.wisc.edu/~sensit/ Sensit [ ref.html#MD04a MD04a]
    Measurement Details
    Usage Scenario
    revision 1
    by mldata on 2010-11-01 11:48

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    Acknowledgements

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