Active learning in robotics: A review of control principles

Active learning is a decision-making process. In both abstract and physical settings, active learning demands both analysis and action. This is a review of active learning in robotics, focusing on methods amenable to the demands of embodied learning systems. Robots must be able to learn efficiently and flexibly through continuous online deployment. This poses a distinct set of control-oriented challenges—one must choose suitable measures as objectives, synthesize real-time control, and produce analyses that guarantee performance and safety with limited knowledge of the environment or robot itself. In this work, we survey the fundamental components of robotic active learning systems. We discuss classes of learning tasks that robots typically encounter, measures with which they gauge the information content of observations, and algorithms for generating action plans. Moreover, we provide a variety of examples – from environmental mapping to nonparametric shape estimation – that highlight the qualitative differences between learning tasks, information measures, and control techniques. We conclude with a discussion of control-oriented open challenges, including safety-constrained learning and distributed learning.     

基于APP软件微课程的翻转教学的应用研究

本文研究利用APP软件对微课程进行智能管理,实现课外课内的学习监控,自主进行学习,并通过各种激励手段引起学生的更大兴趣和关注,从而达到学而优的目的.     

Fictitious play in coordination games

We study the Fictitious Play process with bounded and unbounded recall in pure coordination games for which failing to coordinate yields a payoff of zero for both players. It is shown that every Fictitious Play player with bounded recall may fail to coordinate against his own type. On the other hand, players with unbounded recall are shown to coordinate (almost surely) against their own type as well as against players with bounded recall. In particular, this implies that a FP player's realized average utility is (almost surely) at least as large as his minmax payoff in 2×2 coordination games. Received: December 1997/Final version: November 1998     

径向基函数网络的ABS投影学习算法

Broomhead(1988),Chen(1991)等人提出的RBF网络的学习算法都是基于传统的LMS算法,因此具有一定的局限性。本文提出了一种新的RBF网络的学习算法ABS投影学习算法,它是一种直接的学习算法。计算机模拟的结果表明,它具有学习效率高,识别率高和适用范围广的优点。     

一种改进的Elman神经网络模型

本文首先详细地阐述了Elman神经网络的结构、原理和学习算法.为了进一步提高Elman神经网络的逼近能力和动态特性,我们提出了一种改进的Elman神经网络模型.这种新的Elman神经网络在关联节点与输出节点之间又增加了一组可调权值,利用误差回馈原理推导出了其相应的学习算法.仿真实验结果表明,改进的Elman神经网络比原来的网络具有更好的动态性能,对于贯序输入输出数据的逼近收敛速度更快.     

一类学习联想神经网络的动态特性研究

本文讨论了一类联想神经网络在学习过程中结构变化引起网络平衡点状态变化的动态特性;研究了网络的指数稳定性质;分析了联想过程中输入激励在噪声干扰情况下,网络的滤波能力;得到了一些对于研究联想神经网络学习性质有用的结论。     

广义LVQ算法及其在遥感影像分类中的应用研究

在比较了学习矢量量化 (LVQ)算法和广义学习矢量量化(GLVQ)算法的基础上,建立了基于GLVQ的遥感影像分类模型。以实际土地覆盖分类为例,通过与传统统计方法和LVQ分类器比较,GLVQ分类器具有分类正确率高,收敛速度快,适应范围广等优点。     

遗传算法在Web关联挖掘中的应用研究

关联规则是捕述Web用户行为特征的重要依据。传统的基于统计学的Web关联规则挖掘算法注重对现有数据的分析，不能针对Web关联挖掘的特点提供有效的预测手段和优化反馈措施。本文提出一种基于遗传算法的Web关联挖掘算法体系，实验证明它能。弥补传统Web关联挖掘算法的不足，为Web关联挖掘提供了一种新的思路。     

大间隔分类学习研究现状

在机器学习领域中,间隔扮演着重要的角色。可以用来度量分类的置信度;其理论泛化界也可用于指导分类算法的设计。近年来,该理论已广泛应用于特征选择,分类器训练和集成学习。实际上,间隔思想表明如果在训练阶段模型能够产生大的间隔,那么分类任务将有好的置信度和高可靠性。文中介绍大间隔分类学习方法的研究现状,并给出了其存在的问题。