MRW

Inspired by the theory of simple random walk, we propose a unified framework for semi-supervised learning, i.e. multilevel random walk ( MRW). MRW can produce numerous multi-class label propagation algorithms by constructing various component random walk models based on prior knowledge.

在简单随机游走理论的启发下，我们提出一个统一的半监督学习框架&多层随机游走，通过构建各种组件级的随机游走模型，可以产生一系列对应的多分类标签传播算法。

Although the ways of MRW have better ability to interpret the convergence of economic growth, it supposed that all the regions have same rate of technique progress.

尽管MRW分析框架有很强的解释能力，但它是建立在各个地区技术进步率相同的基础上。

Based on basic economy convergence model, paper introduces the Solow model included human capital, that is, MRW model, which builds convergence mechanism model about human capital to economy convergence.

在基本经济收敛模型基础上，介绍了包括人力资本的扩展Solow模型，即MRW模型，建立人力资本对经济收敛的机制模型。

So, a video watermarking algorithm ( so-called mean value reference watermarking, MRW) with real-time and robustness property is proposed.

为此提出一个能同时满足实时性和鲁棒性的算法&均值参考视频水印（MRW）算法。

It is demonstrated that MRW not only covers most of the existing label propagation algorithms, but also can produce more novel component propagation algorithms, whose solutions can also be recovered under the regularization framework.

多层随机游走框架不仅囊括了目前为止的绝大多数标签传播算法，而且还能产生更多新的高效的组件传播算法，这些结果被我们从正则化的角度得到进一步的证实。