Description: Description: C4.5Rule-PANE is a rule learning method which could generate accurate and comprehensible symbolic rules, through regarding a neural network ensemble as a pre-process of a rule inducer.
Reference: Z.-H. Zhou and Y. Jiang. Medical diagnosis with C4.5 rule preceded by artificial neural network ensemble. IEEE Transactions on Information Technology in Biomedicine, 2003, vol.7, no.1, pp.37-42.
使用神经网络集成方法诊断糖尿病,肝炎,乳腺癌症的案例研究.
-Description: C4.5Rule-PANE is a rule learning method which could generate accurate and comprehensible symbolic rules, through regarding a neural network ensemble as a pre-process of a rule inducer. Reference: Z.-H. Zhou and Y. Jiang. Medical diagnosis with C4.5 rule preceded by artificial neural network ensemble. IEEE Transactions on Information Technology in Biomedicine, 2003, vol.7, no.1, pp.37-42. Platform: |
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Author:修宇 |
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Description: Boosting is a meta-learning approach that aims at combining an ensemble of weak classifiers to form a strong classifier. Adaptive Boosting (Adaboost) implements this idea as a greedy search for a linear combination of classifiers by overweighting the examples that are misclassified by each classifier. icsiboost implements Adaboost over stumps (one-level decision trees) on discrete and continuous attributes (words and real values). See http://en.wikipedia.org/wiki/AdaBoost and the papers by Y. Freund and R. Schapire for more details [1]. This approach is one of most efficient and simple to combine continuous and nominal values. Our implementation is aimed at allowing training from millions of examples by hundreds of features in a reasonable time/memory.
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Author:njustyw |
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Description: 该程序是用matlab写的一个利用遗传算法的选择性集成算法-The program is written in a matlab genetic algorithm using selective Ensemble Learning algorithm Platform: |
Size: 5120 |
Author:eric |
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Description: 聚类分析工具箱 亚历山大博士写的，用于聚类分析，功能比较全-Cluster Analysis and Cluster Ensemble Software
ClusterPack is a collection of Matlab functions for cluster analysis. It consists of the three modules ClusterVisual, ClusterBasics, and ClusterEnsemble as described in the following. They are a selection out of my personal codebase for machine learning research. They contain general clustering algorithms as well as special algorithms developed in my research as indicated in the README files Platform: |
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Author:王鹏 |
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Description: 该源代码主要是利用bagging,adboosting等集成学习的方法进行图像融合处理，效果甚好！-The main source is the use of bagging, adboosting ensemble learning methods such as image fusion, the effect is very good! Platform: |
Size: 26624 |
Author:孟子清 |
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Description: 机器学习大牛周志华教授的关于“整体学习”的文章算法的实现。整体学习可以提高学习机器的推广能力。-Ensemble learning aims to improve generalization
ability by using multiple base learners. It is well-known that
to construct a good ensemble, the base learners should be
accurate as well as diverse. Platform: |
Size: 66560 |
Author:林箫 |
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Description: 主要讲述深度学习的一篇很有用很有用的论文，是最初提出来的，很好，很有帮助-Focuses on the depth of learning a useful useful papers, initially proposed, very good, very helpful Platform: |
Size: 147456 |
Author:haowangli |
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Description: 处理非平衡问题的集成方法，基于随机森林的集成学习-Ensemble learning method,which is based on the random forest classifier, to deal with data imbalance problem Platform: |
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Author:liuzi |
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Description: 集成学习将若干基分类器的预测结果进行综合，具体包括Bagging算法和AdaBoost算法；还有随机森林算法，利用多棵树对样本进行训练并预测的一种分类器-Integrated learning integrates the prediction results of several base classifiers, including Bagging algorithm and AdaBoost algorithm and random forest algorithm, using a tree to train the sample and predict a classifier Platform: |
Size: 2048 |
Author:董小鱼 |
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Search - Ensemble Learning