Custom learner function for Adaboost

I am using Adaboost to fit a classification problem. We can do the following:

ens = fitensemble(X, Y, 'AdaBoostM1', 100, 'Tree')

Now 'Tree' is the learner and we can change this to 'Discriminant' or 'KNN'. Each learner uses a certain Template Object Creation Function. More info here.

Is it possible to create your own function and use it as a learner? And how?

I open templateTree.m and templateKNN.m to see how MATLAB define Template Object Creation Function.

function temp = templateKNN(varargin)
     classreg.learning.FitTemplate.catchType(varargin{:});
     temp = classreg.learning.FitTemplate.make('KNN','type','classification',varargin{:});
end

and

function temp = templateTree(varargin)
   temp = classreg.learning.FitTemplate.make('Tree',varargin{:});
end

It shows that MATLAB has a function in FitTemplate named make , if you open this m-file, you will see :

   function temp = make(method,varargin)
        % Check the type of the required argument
        if ~ischar(method)
            error(message('stats:classreg:learning:FitTemplate:make:BadArgs'));
        end

        % Extract type (classification or regression)
        args = {'type'};
        defs = {    ''};
        [usertype,~,modelArgs] = ...
            internal.stats.parseArgs(args,defs,varargin{:});

        % Check usertype
        if ~isempty(usertype)
            usertype = gettype(usertype);
        end

        % Method
        namesclass = classreg.learning.classificationModels();
        namesreg = classreg.learning.regressionModels();
        [tfclass,locclass] = ismember(lower(method),lower(namesclass));
        [tfreg,locreg] = ismember(lower(method),lower(namesreg));
        if ~tfclass && ~tfreg
            error(message('stats:classreg:learning:FitTemplate:make:UnknownMethod', method));
        end
        if     tfclass && tfreg
            method = namesclass{locclass}; % can get it from namesreg too
            type = usertype;

            % If type is not passed for an ensemble method, try to
            % figure it out from learner types. This is useful for
            % users who want to type
            %   fitensemble(X,Y,'Subspace',100,'Discriminant')
            % instead of
            %   fitensemble(X,Y,'Subspace',100,'Discriminant','type','classification')
            if isempty(type) && ismember(method,classreg.learning.ensembleModels())
                [learners,~,~] = internal.stats.parseArgs({'learners'},{},modelArgs{:});
                if ischar(learners) || isa(learners,'classreg.learning.FitTemplate')
                    learners = {learners};
                elseif ~iscell(learners)
                    error(message('stats:classreg:learning:FitTemplate:make:BadLearnerTemplates'));
                end
                L = numel(learners);
                % The user can pass several learner templates, and some
                % of these learners may be appropriate for
                % classification, some for regression, and some for
                % both. The ensemble type cannot be determined
                % unambiguously unless if all learners are appropriate
                % for one type of learning *only*. For example, in 12a
                %   t1 = ClassificationDiscriminant.template
                %   t2 = ClassificationKNN.template
                %   fitensemble(X,Y,'Subspace',10,{t1 t2})
                % is going to work because both discriminant and k-NN
                % can be used for classification only. If you want to
                % mix discriminant and tree, you have to specify the
                % ensemble type explicitly:
                %   t1 = ClassificationDiscriminant.template
                %   t2 = ClassificationTree.template
                %   fitensemble(X,Y,'Bag',10,{t1 t2},'type','classification')
                types = zeros(L,1); % -1 for regression and 1 for classification
                for l=1:L
                    meth = learners{l};
                    if isa(meth,'classreg.learning.FitTemplate')
                        meth = meth.Method;
                    end
                    isc = ismember(lower(meth),lower(namesclass));
                    isr = ismember(lower(meth),lower(namesreg));
                    if ~isc && ~isr
                        error(message('stats:classreg:learning:FitTemplate:make:UnknownMethod', meth));
                    end
                    types(l) = isc - isr;
                end
                if     all(types==1)
                    type = 'classification';
                elseif all(types==-1)
                    type = 'regression';
                end
            end
        elseif tfclass
            method = namesclass{locclass};
            type = 'classification';
        else
            method = namesreg{locreg};
            type = 'regression';
        end

        % Make sure the type is consistent
        if ~isempty(usertype) && ~strcmp(usertype,type)
            error(message('stats:classreg:learning:FitTemplate:make:UserTypeMismatch', method, usertype));
        end

        % Make template
        temp = classreg.learning.FitTemplate(method,modelArgs);
        temp = fillIfNeeded(temp,type);
    end

You must change this function.