sonar.arff

是UCI数据库中的一些有代表性的数据集

% dependent" experiments are marked in the data files.  The reported
% performance is an average over 10 runs with this single division of the
% data set.
% 
% A standard back-propagation network was used for all experiments.  The
% network had 60 inputs and 2 output units, one indicating a cylinder and the
% other a rock.  Experiments were run with no hidden units (direct
% connections from each input to each output) and with a single hidden layer
% with 2, 3, 6, 12, or 24 units.  Each network was trained by 300 epochs over
% the entire training set.
% 
% The weight-update formulas used in this study were slightly different from
% the standard form.  A learning rate of 2.0 and momentum of 0.0 was used.
% Errors less than 0.2 were treated as zero.  Initial weights were uniform
% random values in the range -0.3 to +0.3.
% 
% RESULTS: 
% 
% For the angle independent experiments, Gorman and Sejnowski report the
% following results for networks with different numbers of hidden units:
% 
% Hidden	% Right on	Std.	% Right on	Std.
% Units	Training set	Dev.	Test Set	Dev.
% ------	------------	----	----------	----
% 0	89.4		2.1	77.1		8.3
% 2	96.5		0.7	81.9		6.2
% 3	98.8		0.4	82.0		7.3
% 6	99.7		0.2	83.5		5.6
% 12	99.8		0.1	84.7		5.7
% 24	99.8		0.1	84.5		5.7
% 
% For the angle-dependent experiments Gorman and Sejnowski report the
% following results:
% 
% Hidden	% Right on	Std.	% Right on	Std.
% Units	Training set	Dev.	Test Set	Dev.
% ------	------------	----	----------	----
% 0	79.3		3.4	73.1		4.8
% 2	96.2		2.2	85.7		6.3
% 3	98.1		1.5	87.6		3.0
% 6	99.4		0.9	89.3		2.4
% 12	99.8		0.6	90.4		1.8
% 24     100.0		0.0	89.2		1.4
% 
% Not surprisingly, the network's performance on the test set was somewhat
% better when the aspect angles in the training and test sets were balanced.
% 
% Gorman and Sejnowski further report that a nearest neighbor classifier on
% the same data gave an 82.7% probability of correct classification.
% 
% Three trained human subjects were each tested on 100 signals, chosen at
% random from the set of 208 returns used to create this data set.  Their
% responses ranged between 88% and 97% correct.  However, they may have been
% using information from the raw sonar signal that is not preserved in the
% processed data sets presented here.
% 
% REFERENCES: 
% 
% 1. Gorman, R. P., and Sejnowski, T. J. (1988).  "Analysis of Hidden Units
% in a Layered Network Trained to Classify Sonar Targets" in Neural Networks,
% Vol. 1, pp. 75-89.
%
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% Relabeled values in attribute 'Class'
%    From: R                       To: Rock                
%    From: M                       To: Mine