

Sonar(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 net-
     work to discriminate between sonar signals bounced off
     a metal cylinder and those bounced off a roughly cylin-
     drical rock.

     Each pattern is a set of 60 numbers in the range 0.0 to
     1.0. Each number represents the energy within a partic-
     ular frequency band, integrated over a certain period
     of time. The integration aperture for higher frequen-
     cies occur later in time, since these frequencies are
     transmitted later during the chirp.

     The label associated with each record contains the let-
     ter "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

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

        * http://www.ics.uci.edu/mlearn/MLRepository.html

     and were converted to R format by Evgenia.Dimitri-
     adou@ci.tuwien.ac.at.

_R_e_f_e_r_e_n_c_e_s_:

     1. Gorman, R. P., and Sejnowski, T. J. (1988). "Analy-
     sis of Hidden Units in a Layered Network Trained to
     Classify Sonar Targets" in Neural Networks, Vol. 1, pp.
     75-89.

