ensemble learning

It applies the bagging ensemble learning technique to improve the predictive performance of rare class.

应用集成学习方法Bagging技术来提高稀有类分类性能。

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Ensemble learning is a methodology that combines multiple individual learners, and can get better results.

集成学习通过结合多个学习者可以获得更好的结果。

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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 negotiation decision model based on ensemble learning is presented.

基于集成学习的协商决策模型。

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Meta-learning method makes use of ensemble learning to obtain the global predication module.

元学习方法是采用集成学习的方式来生成最终的全局预测模型。

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Research on Topics of Bioinformatics Employing Ensemble Learning Algorithm

基于集成学习算法的若干生物信息学问题研究

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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。

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Research on Ensemble Learning Algorithm

集成学习算法研究

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Ensemble Learning is combining multiple learned models to solve a problem.

集成学习（Ensemble Learning）是指利用多个学习机解决一个问题。

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Furthermore, efficient emotion recognition methods are also studied based on rough set theory and ensemble learning.

此外，本文还基于粗糙集理论，结合集成学习理论，对人脸表情识别方法进行研究。

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This paper attempts to research on digital image inpainting techniques base on the ensemble learning techniques.

本文利用集成学习技术来研究数字图像修复技术。

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Research and Application of Ensemble Learning Based on Conditional Mutual Information

基于条件互信息的集成学习的研究与应用

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Using Ensemble Learning Strategy to Handle Class Imbalance Problems

集成学习算法在不平衡分类中的应用研究

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Ensemble Learning and Application of Fuzzy Classification Rules Based on Boosting

基于Boosting的模糊分类规则集成学习及应用

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Recent years, some scholars have applied the kernel function to the ensemble learning and achieved good results.

近年来一些学者将核函数应用到集成学习中，取得了良好的效果。

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The Research on Classifier Ensemble Learning for Data Mining

面向数据挖掘的分类器集成研究

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Ensemble learning application in improvement of particle swarm optimization algorithm

集成学习在粒子群优化算法改进中的应用

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Study on Feature Selection and Ensemble Learning Based on Feature Selection for High-Dimensional Datasets

高维数据的特征选择及基于特征选择的集成学习研究

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Project Analysis for Face Recognition Based on Ensemble Learning

基于集成学习的投影分析及其在人脸识别中的应用

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Describes the classification problem in ensemble learning and two main learning methods Bagging and Boosting.

介绍了在集成学习中的分类问题以及两种主要的集成学习方法Bagging和Boosting。

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Experiments show that the algorithm has higher classification performance than the traditional ensemble learning algorithm based on feature selection.

实验表明该算法比传统的基于特征选择的集成学习算法有更高的分类性能。

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Modeling network intrusion detection using rough set data mining and ensemble learning

基于粗糙集数据挖掘和分类集成学习的网络入侵检测模型

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SVM Algorithms for Large Scale Classification Problems Based on Data Partition and Ensemble Learning

基于数据分割和集成学习的大规模SVM分类算法

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Using Parallel Hyperplanes to Partition Training Set for Ensemble Learning

基于训练集平行分割的集成学习算法研究

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Selective ensemble learning is a special paradigm of ensemble learning.

选择性集成就是一种特殊的集成学习的范式。

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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。

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Ensemble learning is a research hotspot in machine learning, which can improve generalization performance of classification algorithm.

集成学习是当前机器学习的一个研究热点，它可以提高分类算法的泛化性能。

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Study of integration method for multiple classifiers on ensemble learning

集成学习的多分类器动态融合方法研究

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And Ensemble learning can not get good performance with only a few labeled training examples.

而对于小数据量的有标记训练样本，集成学习的效果往往又不理想。

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Ensemble learning can significantly improve the generalization ability of learning system.

SVM本身具有的几个缺点，降低了SVM的稳定性和泛化能力，集成学习可显著提高学习系统的泛化能力。

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