How to do multi class classification using Support Vector Machines (SVM)

Question

In every book and example always they show only binary classification (two classes) and new vector can belong to any one class.

Here the problem is I have 4 classes(c1,

Answer 1

Nothing special compared with binary prediction. see the following example for 3-class prediction based on SVM.

install.packages("e1071")
library("e1071")
data(iris)
attach(iris)
## classification mode
# default with factor response:
model <- svm(Species ~ ., data = iris)
# alternatively the traditional interface:
x <- subset(iris, select = -Species)
y <- Species
model <- svm(x, y) 
print(model)
summary(model)
# test with train data
pred <- predict(model, x)
# (same as:)
pred <- fitted(model)
# Check accuracy:
table(pred, y)
# compute decision values and probabilities:
pred <- predict(model, x, decision.values = TRUE)
attr(pred, "decision.values")[1:4,]
# visualize (classes by color, SV by crosses):
plot(cmdscale(dist(iris[,-5])),
     col = as.integer(iris[,5]),
     pch = c("o","+")[1:150 %in% model$index + 1])

Answer 2

For multi class classification using SVM; It is NOT (one vs one) and NOT (one vs REST).

Instead learn a two-class classifier where the feature vector is (x, y) where x is data and y is the correct label associated with the data.

The training gap is the Difference between the value for the correct class and the value of the nearest other class.

At Inference choose the "y" that has the maximum value of (x,y).

y = arg_max(y') W.(x,y') [W is the weight vector and (x,y) is the feature Vector]

Please Visit link: https://nlp.stanford.edu/IR-book/html/htmledition/multiclass-svms-1.html#:~:text=It%20is%20also%20a%20simple,the%20label%20of%20structural%20SVMs%20.