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data/tanveer/conn-bench-sonar-mines-rocks/sonar.names

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+NAME: Sonar, Mines vs. Rocks
+
+SUMMARY: This is the data set used by Gorman and Sejnowski in their study
+of the classification of sonar signals using a neural network [1].  The
+task is to train a network to discriminate between sonar signals bounced
+off a metal cylinder and those bounced off a roughly cylindrical rock.
+
+SOURCE: The data set was contributed to the benchmark collection by Terry
+Sejnowski, now at the Salk Institute and the University of California at
+San Deigo.  The data set was developed in collaboration with R. Paul
+Gorman of Allied-Signal Aerospace Technology Center.
+
+MAINTAINER: Scott E. Fahlman
+
+PROBLEM DESCRIPTION:
+
+The file "sonar.mines" contains 111 patterns obtained by bouncing sonar
+signals off a metal cylinder at various angles and under various
+conditions.  The file "sonar.rocks" contains 97 patterns obtained from
+rocks under similar conditions.  The transmitted sonar signal is a
+frequency-modulated chirp, rising in frequency.  The data set contains
+signals obtained from a variety of different aspect angles, spanning 90
+degrees for the cylinder and 180 degrees for the rock.
+
+Each pattern is a set of 60 numbers in the range 0.0 to 1.0.  Each number
+represents the energy within a particular frequency band, integrated over
+a certain period of time.  The integration aperture for higher frequencies
+occur later in time, since these frequencies are transmitted later during
+the chirp.
+
+The label associated with each record contains the letter "R" if the object
+is a rock and "M" if it is a mine (metal cylinder).  The numbers in the
+labels are in increasing order of aspect angle, but they do not encode the
+angle directly.
+
+METHODOLOGY: 
+
+This data set can be used in a number of different ways to test learning
+speed, quality of ultimate learning, ability to generalize, or combinations
+of these factors.
+
+In [1], Gorman and Sejnowski report two series of experiments: an
+"aspect-angle independent" series, in which the whole data set is used
+without controlling for aspect angle, and an "aspect-angle dependent"
+series in which the training and testing sets were carefully controlled to
+ensure that each set contained cases from each aspect angle in
+appropriate proportions.
+
+For the aspect-angle independent experiments the combined set of 208 cases
+is divided randomly into 13 disjoint sets with 16 cases in each.  For each
+experiment, 12 of these sets are used as training data, while the 13th is
+reserved for testing.  The experiment is repeated 13 times so that every
+case appears once as part of a test set.  The reported performance is an
+average over the entire set of 13 different test sets, each run 10 times.
+
+It was observed that this random division of the sample set led to rather
+uneven performance.  A few of the splits gave poor results, presumably
+because the test set contains some samples from aspect angles that are
+under-represented in the corresponding training set.  This motivated Gorman
+and Sejnowski to devise a different set of experiments in which an attempt
+was made to balance the training and test sets so that each would have a
+representative number of samples from all aspect angles.  Since detailed
+aspect angle information was not present in the data base of samples, the
+208 samples were first divided into clusters, using a 60-dimensional
+Euclidian metric; each of these clusters was then divided between the
+104-member training set and the 104-member test set.  
+
+The actual training and testing samples used for the "aspect angle
+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.