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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.
It allows for the efficient computation of gradients through random variables, enabling the optimization of parametric probability models using stochastic gradient descent, and the variance reduction of estimators. It was developed in the 1980s in operations research, under the name of "pathwise gradients", or "stochastic gradients".
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
If is chosen to be large, the amount with which the weights change depends heavily on the gradient estimate, and so the weights may change by a large value so that gradient which was negative at the first instant may now become positive. And at the second instant, the weight may change in the opposite direction by a large amount because of the ...
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 ...
Strictly speaking, the term backpropagation refers only to an algorithm for efficiently computing the gradient, not how the gradient is used; but the term is often used loosely to refer to the entire learning algorithm – including how the gradient is used, such as by stochastic gradient descent, or as an intermediate step in a more ...
Empirically, feature scaling can improve the convergence speed of stochastic gradient descent. In support vector machines, [2] it can reduce the time to find support vectors. Feature scaling is also often used in applications involving distances and similarities between data points, such as clustering and similarity search.