You are an expert in writing code in many different languages. Your goal is to perform code completion for the following code, keeping in mind the rest of the code and the file meta data.
    Metadata details:
    filename: smlr_learning.m
    path of file: ./repos/malini/Classifiers/SLR/Classifier_Files/SLR
    
    the code of the file until where you have to start completion: function [w, ix_eff, W, AX] = smlr_learning(label, X, Nfeat, varargin)
% Learning parameters of ARD-sparse multinomial logistic regression model.
%
% The estimation algorthm is derived from variational Bayesian method with 
% Laplace approximation in W-stap (SLR-Laplace).
% Note that the label vectors must be consisting of {0,1} value.
%
% -- Usage
% [w, ix_eff, W, AX] = smlr_learning(label, X, Nfeat, varargin)
% 
% -- Input
% label : Teacher label vector consisting of {0,1}  [N*1]
% X     : Explanatory matrix                        [N*#feature]
% Nfeat : Number of features per class 
%
% -- Field of Optional Input
% w0    : Initial value of weight parameter w 
% ax0   : Initial value of relevance parameter ax
% nlearn : # of learning 
% amax   : Truncation criteria. Parameters whose relevance parameter is larger 
%          than this value are eliminated from further iterations.
% wmaxiter : # of learning in W-step
% wdisplay : 
%            
% -- Output
% w      : Estimated parameters
% ix_eff : Index of the effective feature
% W      : History of parameter learning
% AX     : History of hyper parameter learning
%
% 2009/06/01 OY ver1.00
%
% Copyright (c) 2009, Okito Yamashita, ATR CNS, oyamashi@atr.jp.



    
function [w, ix_eff, W, AX] = smlr_learning(label, X, Nfeat, varargin)
% Learning parameters of ARD-sparse multinomial logistic regression model.
%
% The estimation algorthm is derived from variational Bayesian method with 
% Laplace approximation in W-stap (SLR-Laplace).
% Note that the label vectors must be consisting of {0,1} value.
%
% -- Usage
% [w, ix_eff, W, AX] = smlr_learning(label, X, Nfeat, varargin)
% 
% -- Input
% label : Teacher label vector consisting of {0,1}  [N*1]
% X     : Explanatory matrix                        [N*#feature]
% Nfeat : Number of features per class 
%
% -- Field of Optional Input
% w0    : Initial value of weight parameter w 
% ax0   : Initial value of relevance parameter ax
% nlearn : # of learning 
% amax   : Truncation criteria. Parameters whose relevance parameter is larger 
%          than this value are eliminated from further iterations.
% wmaxiter : # of learning in W-step
% wdisplay : 
%            
% -- Output
% w      : Estimated parameters
% ix_eff : Index of the effective feature
% W      : History of parameter learning
% AX     : History of hyper parameter learning
%
% 2009/06/01 OY ver1.00
%
% Copyright (c) 2009, Okito Yamashita, ATR CNS, oyamashi@atr.jp.


%% Error check
if nargin < 3
    help smlr_learning
end