learning algorithm的例句大全

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。

A learning algorithm based on Lyapunov stability is derived to fuzzy relational model identification in this paper.

本文在李雅普诺夫稳定性意义下，提出了一种辨识模糊关系模型的学习算法。

The advantages and weaknesses of each type time delay compensation algorithm are analyzed, leading to the iterative learning algorithm. Thirdly, the effect of the network time delay to the linear system is analyzed and the time delay composition model is built.

分析每类时滞补偿算法的优点和不足，并引出迭代学习算法。再次，分析网络时滞对线性系统的影响，对时滞进行分析和建模。

Accordingly, the incremental random forest is applied to visual tracking as core learning algorithm under particle filter framework. The classifier is updated by sequentially arrived samples during tracking process.

本文将上述增量随机森林作为鉴别型表观模型应用于视觉目标跟踪，在跟踪过程中提取新的正/负样本不断更新分类器。

A new learning algorithm of fuzzy classification rules is presented based on ensemble learning algorithm.

基于集成学习提出了一种新的模糊分类规则的产生算法。

Research on Ensemble Learning Algorithm

集成学习算法研究

The existing distributed machine learning algorithm library, Mahout, offers some classic classification algorithm, such as Bayes and decision trees.

现有的分布式机器学习算法库Mahout，提供了一些经典的分类挖掘算法，如贝叶斯、决策树等。

A transfer learning algorithm based on visual phrases is proposed.

第三，提出一种基于视觉短语的迁移学习算法。

After carefully studying the assumption, loss function, optimization equation and learning algorithm, we conduct cross domain learning to rank at instance level and feature level.

在系统分析排序学习算法的基本假设、损失函数、优化公式和学习算法之后，本文分别在实例和特征两个方面进行迁移学习，给出各自的基本假设、优化公式以及学习算法。

With the purpose of constructing a large scale of KBS for high performance learning algorithm, a multi-concept learning algorithm is realized in this paper. The learnable performance and complexity of the algorithm are analyzed and the advantages of this algorithm proved by experiments.

为满足大规模知识库系统建设对于高性能学习算法的需要，本文实现了一个面向通用问题的多概念学习算法，分析了算法的可学习性和复杂性，并通过实验验证了该算法的优点。

Thus, we propose a transfer learning algorithm for text classification based on clustering. This algorithm make use of cluster technology to find the instance that can help the learning of the new data from the auxiliary data.

提出一种基于聚类的文本迁移学习算法，利用聚类技术从原领域中寻找能够帮助目标数据学习的实例，用来帮助目标领域的学习。

Improving the SA Learning Algorithm

BP网络局部学习速率自适应SA算法的改进

It uses semi-supervised learning algorithm based on FCM, and for feature selection.

该方案使用的算法就是基于FCM的半监督学习算法，同时可以进行特征选择。

The ANN can derive a continuous-spread stability index to indicate the relative stability degree by means of a semi-supervised learning algorithm.

使用一个半监督学习算法，ANN可产生一个能够指示相对稳定度的连续分布的暂态稳定指标。

In this thesis, we introduce a new semi-supervised learning algorithm: Semi-supervised LPP algorithm, which efficiently combines the characters of both SVM and LPP.

本论文提出了一种有效地结合SVM和LPP的半监督学习算法&LPP半监督算法。

The algorithm is based on Bayesian decision machinery, with estimation of the structure from probability density function, for two-class problems. ( 3) The paper gives out a semi-supervised learning algorithm based on FCM.

该算法基于贝叶斯决策理论，通过概率密度函数进行分布估计，对两类别半监督学习问题进行判定。（3）给出了一个基于FCM的半监督学习算法。

A Semi-Supervised Learning Algorithm Based on Lie Group and Application

一种基于李群的半监督学习算法及应用研究

Propose a semi-supervised learning algorithm based on ensemble learning.

提出了一种基于分类器集成的半监督学习算法。

This model uses support vector machines ( SVM) which has strong learning ability and needs only less number of samples as its learning algorithm, and uses the characteristic quantities which could characterize the furnace mouth flame spectral data to improve the efficiency of model training.

该模型采用学习能力强、对样本数目要求不高的支持向量机作为学习算法，以能表征炉口火焰光谱数据的特征量作为样本数据来提高模型的训练效率。

A weighted class incremental learning algorithm is presented, which improves the CIL algorithm. The algorithm adds the weighs of class to training samples.

提出了一种加权类增量学习算法，该算法是对CIL算法的改进，通过加入类权值，解决了因两类训练样本不平衡而造成的小类别分类精度较低的问题。