Functional gradient descent for financial time series with an application to the measurement of market risk - Audrino, Barone-Adesi - 2005 () Citation Context ...on algorithm in function space, the FGD representation of boosting has been applied also to settings different from classification. ...
李宏毅机器学习之Gradient Descent 一、Review:梯度下降法 在之前回归问题第三步中,需要解决下面最优化的问题: L:lossfunction(损失函数) :parameters(参数) 这里parameters是复数,即指代一堆参数,比如前面说到的w和b 我们需要找到一组参数,让损失函数越小越好,这个问题可以用梯度下降来解决: 假设中有两个参数和随机...
is not “direct” as in Gradient Descent, but may go “zig-zag” if we are visuallizing the cost surface in a 2D space. However, it has been shown that Stochastic Gradient Descent almost surely converges to the global cost minimum if the cost function is convex (or pseudo-convex)[1]...
Generally this approach is called functional gradient descent or gradient descent with functions. One way to produce a weighted combination of classifiers which optimizes [the cost] is by gradient descent in function space —Boosting Algorithms as Gradient Descent in Function Space[PDF], 1999 The ou...
随机梯度下降的方法可以让训练更快速,传统的gradient descent的思路是看完所有的样本点之后再构建loss function,然后去update参数;而stochastic gradient descent的做法是,看到一个样本点就update一次,因此它的loss function不是所有样本点的error平方和,而是这个随机样本点的error平方...
Gradient Descent 梯度下降法是为了求出最小的Loss Function而开始使用的,下面介绍几种常用的梯度下降法 Adagrad 理论上来说,随着梯度越来越趋向于0,学习率也应该越小越好,对于不同的参数来说,学习率也不应该是相同的。所以发明了这一种方法: 这个公式的意义是,分子代表给大的梯度大的学习率,分母起到了相反的效果...
network.Gradient descentis, in fact, a general-purpose optimization technique that can be applied whenever the objective function is differentiable. Actually, it turns out that it can even be applied in cases where the objective function is not completely differentiable through use of a device ...
简述:这篇文章通过推导得到了一个path kernel function,这个kernel function是由gradient descent算法引出的。从kernel function的角度来看,任意两个数据 x 和x′ 原本应该在Euclidean space来比较他们的similarity,现在都在dual space中比较他们的similarity了。假设...
Indeed, our criterion focuses specifically on the top-rank yielding a better precision in the top k positions. A perspective of this work would be to optimize other interesting measures for learning to rank such as NDCG by means of a stochastic gradient descent approach. Another direction, would...
We show analytically that training a neural network by conditioned stochastic mutation or neuroevolution of its weights is equivalent, in the limit of small mutations, to gradient descent on the loss function in the presence of Gaussian white noise. Averaged over independent realizations of the learni...