classificationSVM error for SVM model

in this code the writer used svmclassify but matlab removed this function so I used ClassificationSVM but I get this error :

Error using ClassificationSVM (line 249)

Use fitcsvm to train an SVM model.

Error in main (line 197)

species = ClassificationSVM(svmStruct,Feature);

----------------------------

%%
load DB
load svm
cl = {'open','close'};

dim = [30 60;
        30 60
        40 65];
delete(imaqfind)
vid=videoinput('winvideo',1);
triggerconfig(vid,'manual');
set(vid,'FramesPerTrigger',1 );
set(vid,'TriggerRepeat', Inf);
% start(vid);

%  View the default color space used for the data 
color_spec=vid.ReturnedColorSpace;

% Modify the color space used for the data 
if  ~strcmp(color_spec,'rgb')
    set(vid,'ReturnedColorSpace','rgb');
end

start(vid)


% objects
faceDetector = vision.CascadeObjectDetector;   
faceDetectorLeye = vision.CascadeObjectDetector('EyePairBig'); 
faceDetectorM = vision.CascadeObjectDetector('Mouth'); 
tic
% Initialise vector
LC = 0; 
RC = 0; 
MC = 0; 
TF = 0; 
TC = 0; 
Feature = [];
c1p = 1;
species = 'Non-Fatigue';
for ii = 1:600
   
    trigger(vid);
    im=getdata(vid,1); % Get the frame in im
    imshow(im)
    
    subplot(3,4,[1 2 5 6 9 10]);
    imshow(im)
    
    % Detect faces
    bbox = step(faceDetector, im); 
    
    if ~isempty(bbox);
        bbox = bbox(1,:);

        % Plot box
        rectangle('Position',bbox,'edgecolor','r');

         S = skin_seg2(im);
    
        % Segment skin region
        bw3 = cat(3,S,S,S);

        % Multiply with original image and show the output
        Iss = double(im).*bw3;

        Ic = imcrop(im,bbox);
        Ic1 = imcrop(Iss,bbox);
        subplot(3,4,[3 4]);
        imshow(uint8(Ic1))
        
        bboxeye = step(faceDetectorLeye, Ic); 
        
        if ~isempty(bboxeye);
            bboxeye = bboxeye(1,:);

            Eeye = imcrop(Ic,bboxeye);
            % Plot box
            rectangle('Position',bboxeye,'edgecolor','y');
        else
            disp('Eyes not detected')
        end
        
        if isempty(bboxeye)
            continue;
        end
       Ic(1:bboxeye(2)+2*bboxeye(4),:,:) = 0; 

        % Detect Mouth
        bboxM = step(faceDetectorM, Ic); 
        

        if ~isempty(bboxM);
            bboxMtemp = bboxM;
            
            if ~isempty(bboxMtemp)
            
                bboxM = bboxMtemp(1,:);
                Emouth =  imcrop(Ic,bboxM);

                % Plot box
                rectangle('Position',bboxM,'edgecolor','y');
            else
                disp('Mouth  not detected')
                continue;
            end
        else
            disp('Mouth not detected')
            continue;
        end
        
        [nre nce k ] = size(Eeye);
        
        % Divide into two parts
        Leye = Eeye(:,1:round(nce/2),:);
        Reye = Eeye(:,round(nce/2+1):end,:);
              
        subplot(3,4,7)
        imshow(edge(rgb2gray(Leye),'sobel'));
        subplot(3,4,8)
        imshow(edge(rgb2gray(Reye),'sobel'));
        
        Emouth3 = Emouth;
        
        
        Leye = rgb2gray(Leye);
        Reye = rgb2gray(Reye);
        Emouth = rgb2gray(Emouth);

        % K means clustering
        X = Emouth(:);
        [nr1 nc1 ] = size(Emouth);
        cid = kmeans(double(X),2,'emptyaction','drop');
        
        kout = reshape(cid,nr1,nc1);
        subplot(3,4,[11,12]);
        
        % Segment
        Ism = zeros(nr1,nc1,3);
%         Ism(:,:,3) = 255;
%         Ism(:,:,3) = 125;
        Ism(:,:,3) = 255;
        
        bwm = kout-1;
        bwm3 = cat(3,bwm,bwm,bwm);
        Ism(logical(bwm3)) = Emouth3(logical(bwm3));
        imshow(uint8(Ism));
        
        % Template matching using correlation coefficient
        % Left eye
        % Resize to standard size
        Leye =  imresize(Leye,[dim(1,1) dim(1,2)]);
        c1 =match_DB(Leye,DBL);
        subplot(3,4,7)
        title(cl{c1})
        
        
        % Right eye
        % Resize to standard size
        Reye =  imresize(Reye,[dim(2,1) dim(2,2)]);
        c2 = match_DB(Reye,DBR);
        subplot(3,4,8)
        title(cl{c2})
        
        % Mouth
        % Resize to standard size
        Emouth =  imresize(Emouth,[dim(3,1) dim(3,2)]);
        c3 = match_DB(Emouth,DBM);
        subplot(3,4,[11,12]);
        title(cl{c3})
        
        
        if c1 == 2
            LC = LC+1;
            if c1p == 1
                TC = TC+1;
            end
        end
        if c2==2
            RC = RC+1;
        end
        if c3 == 1
            MC = MC + 1;
        end

        TF = TF + 1; 
        toc
        if toc>8
            Feature = [LC/TF RC/TF MC/TF TC]
            species = ClassificationSVM(svmStruct,Feature);
            

            tic
            % Initialise vector
            LC = 0; % 
            RC = 0; % 
            MC = 0; % 
            TF = 0; % 
            TC = 0; % 
        end
        subplot(3,4,[1 2 5 6 9 10]);
        if strcmpi(species,'Fatigue')
            text(20,20,species,'fontsize',14,'color','r','Fontweight','bold')
            beep;
        else
            text(20,20,species,'fontsize',14,'color','g','Fontweight','bold')
        end
        c1p = c1;
        pause(0.00005)
    end
end

Answer 1

[Walter Roberson]

ClassificationSVM is a class, not a function that you would call directly.

See the example at

https://www.mathworks.com/help/stats/classificationsvm.html#d120e150387

showing how fitcsvm is used to train the classifier. In order to use the classifier to figure out which target data matches, you would use predict() https://www.mathworks.com/help/stats/classreg.learning.classif.compactclassificationsvm.predict.html

I also recommend reading https://www.mathworks.com/matlabcentral/answers/427309-classify-requires-at-least-3-arguments#answer_445205

Answer 2

[ARUN REJI]

Instead of classificationSVM use predict function

[Walter Roberson]

I believe that is what I recommended 11 months ago. https://www.mathworks.com/matlabcentral/answers/572287-classificationsvm-error-for-svm-model#answer_473338