Difference between paradigm and Algorithm in Machine Learning

Question

Give a class $\mathcal{H}$ of predictors from the set X to $\{0,1\}$. Here each $h \in \mathcal{H}$ is a function from $h: X \longrightarrow \{0,1\}$. The Empirical Risk Minimization is defined as

$$ERM_{\mathcal{H}} \in Arg min_{h \in \mathcal{H}} (L_s(h))$$ Where $S=\{(x_1,y_1,\cdots,(x_m,y_m))\}$ is a sample $L_S(h)$ denotes the loss of hypothesis $h$ on the sample $S$.

The goal of the learner is to find an $h$ with as small loss as possible.

In the above it is paradigm not an algorithm. I am not getting the point why it is not referred as algorithm?

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Answer 1

A paradigm is an abstract concept. It is a general method useful to know to solve a problem or construct algorithms, but not a particular algorithm. In your example, the paradigm is to minimize the empirical risk. But it does not say how to perform the minimization concretely.