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C45Rule-PANE
- Descr iption: 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 diagn
icsiboost-0.3.tar
- 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
Software-faults-prediction-using-multiple-classif
- Abstract—In recent years, the use of machine learning algorithms (classifiers) has proven to be of great value in solving a variety of problems in software engineering including software faults prediction. This paper extends the idea of predi
OCD--code
- 通过对集成误差公式的理论分析，提出了一种能主动引导个体网络进行差异性学习的集成网络学习算法。该方法通过对集成误差的分解，使个体网络的训练准则函数中包含个体网络误差相关度的因素，并通过协同训练，引导个体网络进行差异性学习。该方法在基于油气分析的变压器故障诊断的实验结果表明，该方法的故障诊断准确率优于传统的三比值法与BP神经网络，其性能也比经典的集成方法Bagging和Boosting方法更稳定可靠。-A learning algorithm is proposed in this paper by
multi-class-problem
- 将多类别问题分解成多个二类别问题是解决多类别分类问题的常用方式。传统one against all（OAA）分解方式的性能更多的依赖于个体分类器的精度，而不是它的差异性。本文介绍一种基于集成学习的适于多类问题的神经网络集成模型，其基本模块由一个OAA方式的二类别分类器和一个补充多类分类器组成。测试表明，该模型在多类问题上比其他经典集成算法有着更高的精度，并且有较少存储空间和计算时间的优势。-Decompose multi-class problem into multiple binary cl
Neural-Network-Ensemble
- 神经网络集成，集成了最近几年一些关于神经网络发展的文献，适合深入了解的筒子学习-Neural network integration, integration in recent years some of the literature on the development of neural networks, suitable for in-depth understanding of the cheese learning
Ensemble
- javafx的例子代码，有兴趣学习的话是很好的学习资料，这是一个多个图像变换的例子-javafx example code, learning is a good learning materials, which is more than one image transformation example
clusterensemble
- 聚类集成学习方法，提高算法的健壮性，聚类效果比一般算法好-Clustering ensemble learning methods to improve the robustness of the algorithm, the clustering effect is better than the general algorithm
Cluster
- 聚类集成学习方法，提高算法的健壮性，聚类效果比一般算法好-Clustering ensemble learning methods to improve the robustness of the algorithm, the clustering effect is better than the general algorithm
Machine_Learning
- 包括无监督和监督的机器学习技术 • K-means and other clustering tools • Neural Networks • Decision trees and ensemble learning • Naï ve Bayes Classification • Linear, logistic and nonlinear regression-Highlights include unsu
Ensemble-Classifier-for-Concept-Drift-Data-Stream
- In this era an emerging filed in the data mining is data stream mining. The current research technique of the data stream is classification which mainly focuses on concept drift data. In mining drift data with the single classifier is not sufficient
adaptive_adaboosting
- 集成学习中，自适应adaboost算法，可以下载下来参考一下-ensemble learning, adaptive adaboost algorithm, can download the reference
ENtool
- the toolbox contains a good functions for ensemble learning.
robustlssvm
- 鲁棒最小二乘双支持向量机集成学习算法，对于初学者的理解应用特别好用-Robust least squares double support vector machine ensemble learning algorithm for beginners to understand the application of special good use
sentiment_analysis_on_online_customer_reviews
- The project is to determine how much a particular factor influences on the helpfulness of a review. We extracted features like polarity, rating, average word length, helpfulness ratio the collected amazon data. We used gradient boosting classifie
CNTK
- 在深度的重要性的驱使下，出现了一个新的问题：训练一个更好的网络是否和堆叠更多的层一样简单呢？解决这一问题的障碍便是困扰人们很久的梯度消失/梯度爆炸，这从一开始便阻碍了模型的收敛。归一初始化（normalized initialization）和中间归一化（intermediate normalization）在很大程度上解决了这一问题，它使得数十层的网络在反向传播的随机梯度下降（SGD）上能够收敛。 当深层网络能够收敛时，一个退化问题又出现了：随着网络深度的增加，准确率达到饱和（不足为奇）然后迅
boosting_demo
- boosting算法用于集成学习，包含多种弱分类器(Boosting algorithm is used for ensemble learning, and it contains many weak classifiers)
xgboost
- 集成学习，它是二阶求导，速度快，可实现并行计算(Ensemble learning, which is a two order derivative, is fast and can be implemented in parallel computing)
数据挖掘 R语言实战-代码
- 数据挖掘算法R语言实现，包括聚类、判别、集成学习、随机森林、神经网络、支持向量机等算法。(Data mining algorithm R language implementation, including clustering, discrimination, ensemble learning, random forest, neural network, support vector machines and other algorithms.)