We present an adaptive online gradient descent algorithm to solve online convex optimization problems with long-term constraints, which are constraints that need to be satisfied when accumulated over a finite number of rounds T, but can be violated in intermediate rounds. For some user-defined ...
We present an online adaptive distributed controller, based on gradient descent of a Voronoi-based cost function, that generates these closed paths, which the robots can travel for any coverage task, such as environmental mapping or surveillance. The controller uses local information only, and ...
The popular weight optimization methods include gradient descent algorithm [21], GA [22] and PSO [23]. A major limitation of conventional weighted voting approach is that it is a static approach. Show abstract Taxonomy for characterizing ensemble methods in classification tasks: A review and ...
AnAdaptive Gradient (AdaGrad) Algorithmis agradient descent-based learning algorithmwith alearning rateperparameter. Context: It was first developed byDuchi et al., (2011). … References 2018a (ML Glossary, 2018) ⇒ (2008).AdaGrad. In: Machine Learning Glossaryhttps://developers.google.com/ma...
1.复合目标镜像下降法 (Composite Objective Mirror Descent, COMID) COMID 是一种结合了 mirror descent 和 proximal gradient 的优化方法,适用于处理非光滑凸函数。其更新规则如下: wt+1=argminw(gtT(w−wt)+1ηtDψ(w‖wt)+r(w)) 其中: -wt表示第t次迭代时的参数向量。 -gt是在点wt处的目标...
Based on the adaptive reward-shaping mechanism, we propose a novel gradient descent (GD) Sarsa(λ) algorithm to solve the problems of ill initial performance and low convergence speed in the reinforcement learning tasks with continuous state space. Adaptive normalized radial basis function (ANRBF) ...
These so-called relevance factors and the LVQ-network weights (prototypes) are adapted online by means of gradient descent. By comparing the adaptive metrics and investigating the robustness to the noisy supervised information, we show that manipulating the metrics given a prototypical feature ...
Stochastic Gradient Descent (SGD) has become one of the most popular optimization methods for training machine learning models on massive datasets. However... S De,T Goldstein - IEEE International Conference on Data Mining 被引量: 22发表: 2016年 About some works of Boris Polyak on convergence ...
They are error-correction rules, which alter the weights of a network to correct error in the output response to the present input pattern, and gradient rules, which alter the weights of a network during each pattern presentation by gradient descent with the objective of reducing mean-square ...
However, the question of how to effectively select the step-sizes in stochastic gradient descent methods is challenging, and can greatly influence the performance of stochastic gradient descent algorithms. In this paper, we propose a class of faster adaptive gradient descent methods, named AdaSGD, ...