empirical risk minimization in gradient descent
Empirical risk minimization in gradient descent refers to the process of minimizing the average error, or risk, of a model by iteratively adjusting its parameters using gradient descent optimization. This involves calculating the gradients of the model's loss function with respect to its parameters and updating the parameters in the opposite direction of the gradients to gradually reduce the overall error. The goal is to find the optimal set of parameters that best fit the given data and minimize the model's prediction errors.
Requires login.
Related Concepts (1)
Similar Concepts
- batch gradient descent
- conjugate gradient descent
- constraints in gradient descent optimization
- convergence of gradient descent
- gradient descent for linear regression
- gradient descent for neural networks
- mini-batch gradient descent
- non-convex optimization using gradient descent
- online gradient descent
- proximal gradient descent
- regularization techniques in gradient descent
- risk minimization
- second-order methods in gradient descent
- stochastic gradient descent
- variants of gradient descent algorithms