Changbo Zhu and Huan Xu. Online gradient descent in function space, 2015.Zhu, C. and H. Xu (2015). "Online Gradient Descent in Function Space". In: ArXiv e-prints (cited on pages 114, 116, 120, 121, 124).C. Zhu and H. Xu. Online Gradient Descent in Function Space. arXiv.org...
Then, following previous theoretical results bounding the generalization performance of convex combinations of classifiers in terms of general cost functions of the margin, we present a new algorithm (DOOM II) for performing a gradient descent optimization of such cost functions. Experiments on several ...
请注意这里同时出现了descent和boosting,descent指的是stepest-descent minimization,而boosting指的是每一轮迭代过程中的提升。 所以回到Gradient Boosting,从统计学的角度来看,其实也是GAM框架下通过component-wise不断加总迭代的方式,只是每一次迭代时新的回归加总项都是基于上一次迭代时的loss function和base learner之间...
1. Gradient boosting: Distance to target 2. Gradient boosting: Heading in the right direction 3. Gradient boosting performs gradient descent 4. Gradient boosting: frequently asked questions
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...
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 output for the new tree is then added to the output of the existing sequence of trees in an...
Gradient descent generalises naturally to Riemannian manifolds, and to hyperbolic n n -space, in particular. Namely, having calculated the gradient at the point on the manifold representing the model parameters, the updated point is obtained by travelling along the geodesic passing in the direction ...
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...
The drive for autonomy in manufacturing is making increasing demands on control systems, both for improved performance and for extra flexibility. This is r... M Brown,C Harris 被引量: 1005发表: 1993年 Grafting: Fast, Incremental Feature Selection by Gradient Descent in Function Space We present...
在标准的stochastic gradient descent里面, 我们假定用N个可train的parameters, 就像torch.nn里面的...