# Which method should be used to determine the class ID of multiple SVM models?

I'm using Support Vector Machine(SVM) with image classification. Each SVM model results a linear model

$$y = wx + b$$

Where $$w$$ and $$b$$ is the SVM parameters.

If I have multiple SVM models, I will get a vector $$Y$$ and matrix $$W$$ and vector $$B$$

$$Y = Wx + B$$

In my current case, I have a activation function called sign

$$Y = sign(Wx + B)$$

Now, $$Y$$ can holds values such as 1 or -1 only. In my case, $$Y$$ has $$n$$ dimensions (rows) and therefore $$W$$ and $$B$$ are a $$n$$-class SVM model. The index of $$Y$$ that holds the number 1, is the class ID of vector $$x$$.

The problem:

The problem is that what if I get two number 1 inside vector $$Y$$ like $$Y = [-1, -1, 1, -1, -1, -1, 1, -1, -1, -1, -1]$$ ?

I can solve this by using propability. But my data is small, about 150-200 $$Y$$ vectors.

Assume that we are removing the activation function sign and using the pure data of $$Y$$ instead. Which method sould I use here to determine which $$Y_i$$ for a given $$x_i$$, is giving the user the class ID of vector $$x_i$$?

1. Decision trees (multiple if-statements)
2. Naive Bayes Classifier
3. Markov chains
4. Linear regression
5. Other?

The goal here is to find an integer number as class ID for vector $$x$$.