With the "group bootstrap" and two different weighted-vote strategies, the author has studied the application of bagging to rare class problem carefully and proposed some new efficient ensemble learning algorithms.

Then I introduce the theory that weak learners can be boosted to strong learners and elaborate the related concept, principle, advantages of ensemble learning and its two main algorithms: Bagging and Boosting.

阐述了弱可学习理论以及集成学习的相关概念、原理、优势和其两个主要算法：Bagging和Boosting。

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At last, the paper research on the application of an integrated learning, Firstly, Studied the intrusion detection data set ( KDD CUP 99 、 UCI).

最后，本文还对集成学习的应用进行了研究，第一，详细分析了入侵检测数据集（KDD CUP99、UCI）。

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For naive bayes is a stable approach, in order to fit for ensemble learning, to destroy its stable with random Oracle approach and use entropy diversity to measure the diversity of naive bayes ensemble.

由于朴素贝叶斯是一种稳定的学习方法，为了应用集成学习，通过随机Oracle方法破坏其稳定性，利用熵差异性度量研究了朴素贝叶斯集成的差异性。

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Through the experiment on the COD data and compared with some commonly used regression methods, it proves the efficiency and applicability of the ensemble learning Boosting regression algorithm.

通过对一批水质COD数据进行的实验仿真，并将其结果和一些常用的回归方法作了比较，证明了集成学习Boosting回归算法对各种实际问题的有效性和适应性。

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As this algorithm selects feature subset using evaluation criterion based on the margin, and constructs individual classifier with higher accuracy rate, it improves the performance of ensemble learning.

该算法用基于margin的特征子集评估准则来选择特征子集，构造了准确率比较高的个体分类器，提高了集成学习的效果。

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We collect shadow discrimination features from multiple shadow discrimination models. A shadow detector is trained by employing boosting-pruning-Bagging-ensemble-based learning framework.

我们利用多个阴影鉴别模型来构建阴影鉴别特征集合，并使用Boosting修剪Bagging集成学习框架来训练阴影检测器。

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Inspired by ensemble learning methods, a prediction algorithm that combines multiple SVR models is proposed.

本文借鉴集成学习的思想，提出一种混合多个SVR模型的预测算法。

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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 machine learning technique which trains a group of learner for a practical problem, and joins those learners together to execute the prediction task.

集成学习（Ensemble Learning）是为某个问题训练一组学习器，并将这些学习器联合起来执行一定预测任务的一种机器学习技术。

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In this thesis, Boosting is introduced to the SVM, and support vector machines combination classifier is proposed.

本文利用Boosting对支持向量机进行集成学习，并给出了一种基于Boosting的支持向量机组合分类器。

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Ensemble Learning is machine learning function. It is a research focus of the current. Ensemble learning has a very good application in many fields.

集成学习（Ensemble Learning）是一种机器学习范式，是当前的研究热点之一在多个领域有了很好的应用。

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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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Now ensemble learning has successfully applied in many fields, such as planet exploration, seismic wave analysis, Web information filtering, biology feature recognition, and computer aided medical diagnoses.

现在，集成学习已经成功应用于行星探测、地震波分析、Web信息过滤、生物特征识别、计算机辅助医疗诊断等众多领域。

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If the ensemble learning technology can be introduced to SVM, the generalization performance of SVM may be improved efficiently.

将集成学习技术引入到SVM学习中，可以更好地提高SVM的泛化能力。

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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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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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Design and implementation on E_Learning system based on Agent and integrated with study context

基于Agent集成学习情境的E Learning系统设计与实现

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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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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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Geologic Characteristics and Engineering Geologic Problems Of the Dam Foundation of the Fenhe Reservoir ⅱ

汾河二库大坝坝基地质特征与工程地质问题

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