Is the prediction algorithm absolutely the same for all linear classifiers and linear regression algorithms?
As known, any linear classifier can be described as:
y = w1*x1 + w2*x2 + ... + c
There are two broad classes of methods for determining the parameters of a linear classifier (generative and discriminative): https://en.wikipedia.org/wiki/Linear_classifier
- Linear Discriminant Analysis (or Fisher's linear discriminant), Naive Bayes classifier
- Logistic regression, Perceptron, Support vector machine
Question: Is it true that linear classifiers differ only in the Learning algorithm, but do they do the same during Prediction
y = w1*x1 + w2*x2 + ... + c?
If I used one method for Training (for example SVM with linear kernel function), then can I use other method for prediction (for example Perceptron) with the same output result?
And can I do the same, if I used SVR (Support Vector Regression with linear kernel function) for Training, then can I use Perceptor as linear regression method for Prediction value?