random learning

In the field of machine learning, random forest is an important and common method of data mining.

在机器学习领域，随机森林是一种重要和常见的数据挖掘方法。

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Skydiving, talking to that random stranger, learning a foreign language, picking a strange new book, taking Argentinian tango classes, rock climbing, learning how to sew... What new things will you try today?

跳伞、随便跟一个陌生人讲话、学一种外语、挑一本没看过的新书、上阿根廷探戈的课、攀岩、学习缝纫…今天你要挑战哪项新事物呢？

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Random neural network ( RNN) is a special kind of artificial neural network, which is developed recently and has its own peculiarities on the structure, the learning algorithm, the state-updating rule and the applications.

随机神经网络（RNN）在人工神经网络中是一类比较独特、出现较晚的神经网络，它的网络结构、学习算法、状态更新规则以及应用等方面都因此具有自身的特点。

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Research on Complete Random Learning Scheme in Ensemble Learning

集成学习中完全随机学习策略研究

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A new ensemble-learning algorithm-Random Forest is applied in this paper. As a consequence, the selected index system is more objective and more suitable for machine learning.

本文引进组合学习算法的新方法随机森林（Random Forest，RF）来选择指标，使得到的指标体系更加客观，更加符合机器学习的特点。

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And will describe a real-time body parts recognition algorithm which is based on self-made depth image sample library, local gradient feature extraction and combined with random forest learning.

本文主要讨论利用深度图像进行人体部位识别的问题，将阐述一种利用自制的深度图像样本库，提取局域梯度特征，结合随机森林学习的实时人体部位识别算法。

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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。

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This paper elaborates on the low learning efficiency in reinforcement learning due to improper generalization and random exploration policy under deterministic MDPS and proposes a hierarchical reinforcement learning algorithm based on system model.

针对强化学习算法的状态值泛化和随机探索策略在确定性MDP系统控制中存在着学习效率低的问题，本文提出基于模型的层次化强化学习算法。

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Research on the Part-of-Speech Tagging Method& POS Tagging using Conditional Random Fields and Transformation Based Learning

词性标注的方法研究&结合条件随机场和基于转换学习的方法进行词性标注

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The experimental evaluations show that the proposed asymmetric semi-supervised learning methodology in conjunction with random subspace theory is effective to improve CBIR performance and achieves better performance than some existing approaches.

实验结果表明，不对称的半监督学习策略及随机子空间理论可有效地改进SVM反馈性能，且所提出算法明显优于同类SVM反馈方法。

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