搜索资源列表
FASBIR
- Descr iption: FASBIR(Filtered Attribute Subspace based Bagging with Injected Randomness) is a variant of Bagging algorithm, whose purpose is to improve accuracy of local learners, such as kNN, through multi-model perturbing ensemble. Reference:
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
advanced_ensembel
- 神经网络集成的例子!基于南大周志华的论文，用神经网络集成解决异或问题!-neural network ensemble example! South dragon on the thesis, using neural network integration solutions differences or problems!
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
orensemble
- The program implements three large-margin thresholded ensemble algorithms for ordinal regression. It includes an improved RankBoost algorithm, the ORBoost-LR algorithm, and the ORBoost-All algorithm.
FuzzyBPNN
- matlab格式源代码。功能：模糊BP神经网络集成解耦算法和应用于控制优化模型问题。-matlab source code format. Function: fuzzy BP neural network ensemble decoupling control algorithm and optimization model applied to the problem.
EnsembleTracking
- 整体跟踪算法及其实现,可以在线进行跟踪； 同时可以自适应变化-ensemble tracking
CLUSTERING-ENSEMBLE-ALGORITH
- A SURVEY OF CLUSTERING ENSEMBLE ALGORITHMS
MIL-Ensemble
- This toolbox contains re-implementations of four different multi-instance learners, i.e. Diverse Density, Citation-kNN, Iterated-discrim APR, and EM-DD. Ensembles of these single multi-instance learners can be built with this toolbox
NeC45
- NeC4.5 is a variant of C4.5 decision tree, which could generate decision trees more accurate than standard C4.5 decision trees, through regarding a neural network ensemble as a pre-process of C4.5 decision tree.
EnsembleClassifier20081016001935
- ensemble classifier example
gyy
- 从因子分析的角度出发解决基因表达谱分析问题。为解决独立成分分析方法在求解过程中的不稳定性，提出一种基于选择性独立成分分析的DNA微阵列数据集成分类器。首先对基因表达水平的重构误差进行分析，选择部分重构误差较小的独立成分进行样本重构，然后基于重构后的样本同时训练多个支持向量机基分类器，最后选择部分分类正确率较高的基分类器进行最大投票以得到最终结果。在3个常用测试集上验证了本文设计方法的有效性。-This paper tries to deal with gene expression proble
coforest
- CoForest是一种半监督算法，处理集成学习及利用大量未标记数据得到更优越性能的假设。-CoForest is a semi-supervised algorithm, which exploits the power of ensemble learning and large amount of unlabeled data available to produce hypothesis with better performance.
ClusterEnsembleV10
- Alexander Strehl的CLUSTER ENSEMBLE算法----------------------------------------------------------------------- CLUSTERENSEMBLE README Alexander Strehl Version 1.0 2002-04-20 -------------------------------------------------------------------
bpnet
- 简单的BP神经网络集成，使用时直接调用bpnet就行-Simple BP neural network ensemble, used directly on the line call bpnet
bpbpnet
- 研友wangleisxcc的程序基础上，我把初始化网络，训练网络，和网络使用三个稍微集成后的一个新函数bpnet 简单的BP神经网络集成，使用时直接调用bpnet就行-Friends wangleisxcc research process, I make the network initialization, training networks, and network integration using a little after three, a new function bpne
cPP-TSP-Ensemble
- 7个c++版TSP问题,收集到的可用版本。方便大家的学习和使用！-7 c++ version of the TSP problem, collect the available versions. Facilitate learning and use!
random-subspace-classifier-ensemble
- 随机子空间集成分类器, 可以实现比bagging 更好的分类和识别-Random subspace classifier ensemble
ensemble-learing-for-decision-tree
- 决策树的集成学习,用Java语言实现！具有良好的分类性能！-ensemble learning for decision tree
Ensemble Methods Foundations and Algorithms
- This book provides researchers, students and practitioners with an introduction to ensemble methods. The book consists of eight chapters which naturally constitute three parts.