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Today, I would like to share with you how to achieve matlab binary support vector machine to find many kinds of boundaries of the relevant knowledge points, detailed content, clear logic, I believe that most people still know too much about this knowledge, so share this article for your reference, I hope you can learn something after reading this article, let's take a look at it.

Load Iris dataset

Use the length and width of the petals

Load fisheriris

X = meas (:, 3:4)

Y = species

Scatter plot of%% data

Figure

Gscatter (X (:, 1), X (:, 2), Y)

H = gca

Lims = [h.XLim h.YLim]

Title ('{\ bf Scatter Diagram of Iris Measurements}')

Xlabel ('Petal Length (cm)')

Ylabel ('Petal Width (cm)')

Legend ('Location','Northwest')

% has three classes, one of which is linearly separable

%%

% do the following for each category:

Create a logical vector to indicate whether you are a member of this class or not

Use processed data and logic vectors to train SVM classifiers

Store classifier in cell array

It is better to define the order of categories in advance

SVMModels = cell (3jue 1)

Classes = unique (Y)

Rng (1)

For j = 1:numel (classes)

Indx = strcmp (Y classes (j))

Create a binary classifier for each category

SVMModels {j} = fitcsvm, [false true], 'Standardize',true,...

'KernelFunction','rbf','BoxConstraint',1)

End

%%

% | SVMModels | it is a cell array of 3X1

% each cell is a classifier

% the positive values of each classifier are setosa,versicolor and virginica

%% divides the distribution coordinates of the training data into grids and regards them as new observations.

% use each classifier to estimate the score of new observations

D = 0.02

[x1GridJournal x2Grid] = meshgrid (min (X (:, 1)): d:max (X (:, 1)),...

Min (X (:, 2)): d:max (X (:, 2))

XGrid = [x1Grid (:), x2Grid (:)]

N = size (xGrid,1)

Scores = zeros (N _ numel (classes))

For j = 1:numel (classes)

[~, score] = predict (SVMModels {j}, xGrid)

Scores (:, j) = score (:, 2)

The second column contains a positive category score

End

%% there are three scores for each row, and the largest one is the category corresponding to this line.

[~, maxScore] = max (Scores, [], 2)

%% shows the grid corresponding to each category in the figure

Figure

H (1:3) = gscatter (xGrid (:, 1), xGrid (:, 2), maxScore,...

[0.1 0.5 0.5; 0.5 0.1 0.5; 0.5 0.5 0.1])

Hold on

H (4:6) = gscatter (X (:, 1), X (:, 2), Y)

Title ('{\ bf Iris Classification Regions}')

Xlabel ('Petal Length (cm)')

Ylabel ('Petal Width (cm)')

Legend (h, {'setosa region','versicolor region','virginica region',...)

'observed setosa','observed versicolor','observed virginica'},...

'Location','Northwest')

Axis tight

Hold off

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