ksvm-class              package:kernlab              R Documentation

_C_l_a_s_s "_k_s_v_m"

_D_e_s_c_r_i_p_t_i_o_n:

     An S4 class containing the output (model) of the 'ksvm' Support
     Vector Machines function

_O_b_j_e_c_t_s _f_r_o_m _t_h_e _C_l_a_s_s:

     Objects can be created by calls of the form 'new("ksvm", ...)' or
     by calls to the 'ksvm' function.

_S_l_o_t_s:

     '_t_y_p_e': Object of class '"character"'  containing the problem
          support vector machine problem type ("C-svc", "nu-svc",
          "C-bsvc", "spoc-svc", "one-svc", "eps-svr", "nu-svr",
          "eps-bsvr")

     '_p_a_r_a_m': Object of class '"list"' containing the Support Vector
          Machine parameters (C, nu, epsilon)

     '_k_e_r_n_e_l_f': Object of class '"function"' containing the kernel
          function

     '_k_p_a_r': Object of class '"list"' containing the kernel function
          parameters (hyperparameters)

     '_k_c_a_l_l': Object of class '"ANY"' containing the      'ksvm'
          function call

     '_s_c_a_l_i_n_g': Object of class '"ANY"' containing the scaling
          information performed on the data

     '_k_t_e_r_m_s': Object of class '"ANY"' containing the terms
          representation of the symbolic model used (when using a
          formula)

     '_x_m_a_t_r_i_x': Object of class '"matrix"' the data matrix used during
          computations (possibly scaled and whithout NA)

     '_y_m_a_t_r_i_x': Object of class '"ANY"' the response matrix/vector 

     '_f_i_t': Object of class '"ANY"' with the fitted values, predictions
          using the training set.

     '_l_e_v': Object of class '"vector"' with the levels of the response
          (in the case of classifiaction)

     '_p_r_o_b._m_o_d_e_l': Object of class '"list"' with the class prob. model

     '_p_r_i_o_r': Object of class '"list"' with the prior of the training
          set

     '_n_c_l_a_s_s': Object of class '"numeric"'  containing the number of
          classes (in the case of classification)

     '_a_l_p_h_a': Object of class '"ANY"' containing the resulting alpha
          vector (list or matrix in case of multiclass classification)
          (support vectors)

     '_c_o_e_f_f': Object of class '"ANY"' containing the resulting
          coefficients

     '_a_l_p_h_a_i_n_d_e_x': Object of class '"list"' containing

     '_b': Object of class '"numeric"' containing the resulting offset 

     '_S_V_i_n_d_e_x': Object of class '"vector"' containing the indexes of
          the support vectors

     '_n_S_V': Object of class '"numeric"' containing the number of
          suppport vector machines 

     '_e_r_r_o_r': Object of class '"numeric"' containing the training error

     '_c_r_o_s_s': Object of class '"numeric"' containing the
          cross-validation error 

     '_n._a_c_t_i_o_n': Object of class '"ANY"' containing the action
          performed for NA 

_M_e_t_h_o_d_s:

     _S_V_i_n_d_e_x 'signature(object = "ksvm")': return the indexes of
          support vectors

     _a_l_p_h_a 'signature(object = "ksvm")': returns the complete alpha
          vector (wit zero values)

     _a_l_p_h_a_i_n_d_e_x 'signature(object = "ksvm")': returns the indexes of
          non-zero alphas (support vectors)

     _c_r_o_s_s 'signature(object = "ksvm")': returns the cross-validation
          error 

     _e_r_r_o_r 'signature(object = "ksvm")': returns the training error 

     _f_i_t 'signature(object = "ksvm")': returns the fitted values
          (predict on training set) 

     _k_e_r_n_e_l_f 'signature(object = "ksvm")': returns the kernel function

     _k_p_a_r 'signature(object = "ksvm")': returns the kernel parameters
          (hyperparameters)

     _l_e_v 'signature(object = "ksvm")': returns the levels in case of
          classification  

     _p_r_o_b._m_o_d_e_l 'signature(object="ksvm")': returns class prob. model
          values

     _p_r_i_o_r 'signature(object="ksvm")': returns  the prior of the
          training set

     _k_c_a_l_l 'signature(object="ksvm")': returns the 'ksvm' function call

     _s_c_a_l_i_n_g 'signature(object = "ksvm")': returns the scaling values 

     _s_h_o_w 'signature(object = "ksvm")': prints the object information

     _t_y_p_e 'signature(object = "ksvm")': returns the problem type

     _x_m_a_t_r_i_x 'signature(object = "ksvm")': returns the data matrix used

     _y_m_a_t_r_i_x 'signature(object = "ksvm")': returns the response vector

_A_u_t_h_o_r(_s):

     Alexandros Karatzoglou 
      alexandros.karatzolgou@ci.tuwien.ac.at

_S_e_e _A_l_s_o:

     'ksvm',  'rvm-class', 'gausspr-class'

_E_x_a_m_p_l_e_s:

     ## simple example using the promotergene data set
     data(promotergene)

     ## train a support vector machine
     gene <- ksvm(Class~.,data=promotergene,kernel="rbfdot",kpar=list(sigma=0.015),C=50,cross=4)
     gene

     # the kernel  function
     kernelf(gene)
     # the alpha values
     alpha(gene)
     # the coefficients
     coeff(gene)
     # the fitted values
     fit(gene)
     # the cross validation error
     cross(gene)

