Regularized empirical risk minimization (RegERM) is a general concept that defines a family of optimization problems in machine learning, as, e.g., Support Vector Machine, Logistic Regression, and Ridge Regression.
Contents:
api.rst methods.rst
- Let ${\bf x}_i$ be a vector of features describing an instance i and yi be its target value. Then, for a given set of n training instances $\{({\bf x}_i,y_i)\}_{i=1}^n$ the goal is to find a model ${\bf w}$ that minimizes the regularized empirical risk:
$$\sum_{i=1}^n \ell({\bf w}, {\bf x}_i, y_i) + \Omega({\bf w}).$$
The loss function ℓ measures the disagreement between the true label y and the model prediction and the regularizer Ω penalizes the model's complexity.
optimize(method::RegERM, λ::Float64, optimizer::Symbol=:l_bfgs)
Perform the optimization of method for a given regularization parameter λ and return a prediction model that can be used for classification. Stochastic gradient descent (:svg) and Limited-memory BFGS (:l_bfgs) are valid optimizer.
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