Search results
Results from the WOW.Com Content Network
Stochastic gradient descent competes with the L-BFGS algorithm, [citation needed] which is also widely used. Stochastic gradient descent has been used since at least 1960 for training linear regression models, originally under the name ADALINE. [25] Another stochastic gradient descent algorithm is the least mean squares (LMS) adaptive filter.
This technique is used in stochastic gradient descent and as an extension to the backpropagation algorithms used to train artificial neural networks. [29] [30] In the direction of updating, stochastic gradient descent adds a stochastic property. The weights can be used to calculate the derivatives.
Stochastic gradient descent (SGD) BFGS; Conjugate gradient; Regularization (L1 norm, L2 norm, & elastic net regularization) Flexible input - input features may be: Binary; Numerical; Categorical (via flexible feature-naming and the hash trick) Can deal with missing values/sparse-features; Other features
In the stochastic setting, under the same assumption that the gradient is Lipschitz continuous and one uses a more restrictive version (requiring in addition that the sum of learning rates is infinite and the sum of squares of learning rates is finite) of diminishing learning rate scheme (see section "Stochastic gradient descent") and moreover ...
Belle is a chess computer that was developed by Joe Condon (hardware) and Ken Thompson (software) at Bell Labs.In 1983, it was the first machine to achieve master-level play, with a USCF rating of 2250.
In optimization, a gradient method is an algorithm to solve problems of the form min x ∈ R n f ( x ) {\displaystyle \min _{x\in \mathbb {R} ^{n}}\;f(x)} with the search directions defined by the gradient of the function at the current point.
Descent direction; Guess value — the initial guess for a solution with which an algorithm starts; Line search. Backtracking line search; Wolfe conditions; Gradient method — method that uses the gradient as the search direction Gradient descent. Stochastic gradient descent; Landweber iteration — mainly used for ill-posed problems
Deep backward stochastic differential equation method is a numerical method that combines deep learning with Backward stochastic differential equation (BSDE). This method is particularly useful for solving high-dimensional problems in financial derivatives pricing and risk management .