Sonar                package:mlbench                R Documentation

_S_o_n_a_r, _M_i_n_e_s _v_s. _R_o_c_k_s

_D_e_s_c_r_i_p_t_i_o_n:

     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.  

     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.

_U_s_a_g_e:

     data(Sonar)

_F_o_r_m_a_t:

     A data frame with 208 observations on 61 variables, all numerical
     and one (the Class) nominal.

_S_o_u_r_c_e:

        *  Contribution: Terry Sejnowski, Salk Institute and University
           of California, San Deigo.

        *  Development: R. Paul Gorman, Allied-Signal Aerospace
           Technology Center. 

        *  Maintainer: Scott E. Fahlman 

     These data have been taken from the UCI Repository Of Machine
     Learning Databases at

        *  <URL: ftp://ftp.ics.uci.edu/pub/machine-learning-databases>

        *  <URL: http://www.ics.uci.edu/~mlearn/MLRepository.html>

     and were converted to R format by
     Evgenia.Dimitriadou@ci.tuwien.ac.at.

_R_e_f_e_r_e_n_c_e_s:

     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.

