登录
首页 » Java » mulan

mulan

于 2020-12-17 发布 文件大小:976KB
0 133
下载积分: 1 下载次数: 7

代码说明:

  mulan实现多标签分类,内含多个重要的分类器,乃是分类中的精华(Mulan implementation of multi label classification, containing multiple important classifier, is the essence of the classification)

文件列表:

mulan
.....\.classpath,592,2015-08-04
.....\.project,381,2015-07-17
.....\bin
.....\...\emotions.arff,380476,2012-07-26
.....\...\emotions.xml,332,2012-07-26
.....\...\mlknn
.....\...\.....\Main.class,2023,2015-08-07
.....\...\.....\NewMlknn.class,8959,2015-08-10
.....\...\mulan
.....\...\.....\classifier
.....\...\.....\..........\InvalidDataException.class,638,2015-08-04
.....\...\.....\..........\lazy
.....\...\.....\..........\....\BRkNN$ExtensionType.class,1228,2015-08-04
.....\...\.....\..........\....\BRkNN.class,9309,2015-08-04
.....\...\.....\..........\....\IBLR_ML.class,6982,2015-08-04
.....\...\.....\..........\....\MLkNN.class,6547,2015-08-04
.....\...\.....\..........\....\MultiLabelKNN.class,2697,2015-08-11
.....\...\.....\..........\LearnerException.class,625,2015-08-04
.....\...\.....\..........\meta
.....\...\.....\..........\....\ClusteringBased.class,5471,2015-08-04
.....\...\.....\..........\....\ConstrainedKMeans$bucketInstance.class,1246,2015-08-04
.....\...\.....\..........\....\ConstrainedKMeans.class,12102,2015-08-04
.....\...\.....\..........\....\EnsembleOfSubsetLearners$IdComparator.class,1425,2015-08-04
.....\...\.....\..........\....\EnsembleOfSubsetLearners$LabelSubsetsWeight.class,2024,2015-08-04
.....\...\.....\..........\....\EnsembleOfSubsetLearners$SubsetsDistance.class,1956,2015-08-04
.....\...\.....\..........\....\EnsembleOfSubsetLearners.class,19559,2015-08-04
.....\...\.....\..........\....\HierarchyBuilder$Method.class,1289,2015-08-04
.....\...\.....\..........\....\HierarchyBuilder.class,14388,2015-08-04
.....\...\.....\..........\....\HMC.class,12965,2015-08-04
.....\...\.....\..........\....\HMCNode.class,3905,2015-08-04
.....\...\.....\..........\....\HOMER.class,6047,2015-08-04
.....\...\.....\..........\....\MultiLabelMetaLearner.class,783,2015-08-04
.....\...\.....\..........\....\RAkEL.class,7688,2015-08-04
.....\...\.....\..........\....\RAkELd.class,7078,2015-08-04
.....\...\.....\..........\....\SubsetLearner.class,14140,2015-08-04
.....\...\.....\..........\....\thresholding
.....\...\.....\..........\....\............\ExampleBasedFMeasureOptimizer.class,4345,2015-08-04
.....\...\.....\..........\....\............\Meta.class,6343,2015-08-04
.....\...\.....\..........\....\............\MetaLabeler.class,8911,2015-08-04
.....\...\.....\..........\....\............\MLPTO.class,7206,2015-08-04
.....\...\.....\..........\....\............\OneThreshold.class,8163,2015-08-04
.....\...\.....\..........\....\............\RCut.class,7500,2015-08-04
.....\...\.....\..........\....\............\SCut.class,8253,2015-08-04
.....\...\.....\..........\....\............\ThresholdPrediction.class,7430,2015-08-04
.....\...\.....\..........\ModelInitializationException.class,662,2015-08-04
.....\...\.....\..........\MultiLabelLearner.class,553,2015-08-04
.....\...\.....\..........\MultiLabelLearnerBase.class,2826,2015-08-04
.....\...\.....\..........\MultiLabelOutput.class,3841,2015-08-04
.....\...\.....\..........\neural
.....\...\.....\..........\......\BPMLL.class,13580,2015-08-04
.....\...\.....\..........\......\BPMLLAlgorithm.class,7512,2015-08-04
.....\...\.....\..........\......\DataPair.class,3092,2015-08-04
.....\...\.....\..........\......\MMPLearner.class,11988,2015-08-04
.....\...\.....\..........\......\MMPMaxUpdateRule.class,1471,2015-08-04
.....\...\.....\..........\......\MMPRandomizedUpdateRule.class,2979,2015-08-04
.....\...\.....\..........\......\MMPUniformUpdateRule.class,2257,2015-08-04
.....\...\.....\..........\......\MMPUpdateRuleBase.class,2674,2015-08-04
.....\...\.....\..........\......\MMPUpdateRuleType.class,1186,2015-08-04
.....\...\.....\..........\......\model
.....\...\.....\..........\......\.....\ActivationFunction.class,454,2015-08-04
.....\...\.....\..........\......\.....\ActivationLinear.class,924,2015-08-04
.....\...\.....\..........\......\.....\ActivationTANH.class,921,2015-08-04
.....\...\.....\..........\......\.....\BasicNeuralNet.class,4840,2015-08-04
.....\...\.....\..........\......\.....\NeuralNet.class,421,2015-08-04
.....\...\.....\..........\......\.....\Neuron.class,5100,2015-08-04
.....\...\.....\..........\......\ModelUpdateRule.class,321,2015-08-04
.....\...\.....\..........\......\NormalizationFilter.class,3682,2015-08-04
.....\...\.....\..........\......\ThresholdFunction.class,2833,2015-08-04
.....\...\.....\..........\transformation
.....\...\.....\..........\..............\AdaBoostMH.class,1851,2015-08-04
.....\...\.....\..........\..............\BinaryRelevance.class,3519,2015-08-04
.....\...\.....\..........\..............\CalibratedLabelRanking.class,10108,2015-08-04
.....\...\.....\..........\..............\ClassifierChain.class,5590,2015-08-04
.....\...\.....\..........\..............\EnsembleOfClassifierChains.class,6300,2015-08-04
.....\...\.....\..........\..............\EnsembleOfPrunedSets.class,5592,2015-08-04
.....\...\.....\..........\..............\IncludeLabelsClassifier.class,2715,2015-08-04
.....\...\.....\..........\..............\LabelPowerset.class,5025,2015-08-04
.....\...\.....\..........\..............\LabelsetPruning.class,3834,2015-08-04
.....\...\.....\..........\..............\MultiClassLearner.class,2215,2015-08-04
.....\...\.....\..........\..............\MultiLabelStacking.class,16409,2015-08-04
.....\...\.....\..........\..............\PPT$Strategy.class,1219,2015-08-04
.....\...\.....\..........\..............\PPT.class,5943,2015-08-04
.....\...\.....\..........\..............\PrunedSets$Strategy.class,1242,2015-08-04
.....\...\.....\..........\..............\PrunedSets.class,6820,2015-08-04
.....\...\.....\..........\..............\TransformationBasedMultiLabelLearner.class,2291,2015-08-04
.....\...\.....\core
.....\...\.....\....\ArgumentNullException.class,1216,2015-08-04
.....\...\.....\....\MulanException.class,594,2015-08-04
.....\...\.....\....\MulanJavadoc.class,3615,2015-08-04
.....\...\.....\....\MulanRuntimeException.class,622,2015-08-04
.....\...\.....\....\Util.class,1104,2015-08-04
.....\...\.....\....\WekaException.class,604,2015-08-04
.....\...\.....\data
.....\...\.....\....\ConditionalDependenceIdentifier.class,8602,2015-08-04
.....\...\.....\....\ConverterCLUS.class,5825,2015-08-04
.....\...\.....\....\ConverterLibSVM.class,6687,2015-08-04
.....\...\.....\....\DataLoadException.class,616,2015-08-04
.....\...\.....\....\DataUtils.class,1281,2015-08-04
.....\...\.....\....\GreedyLabelClustering.class,8137,2015-08-04

下载说明:请别用迅雷下载,失败请重下,重下不扣分!

发表评论

0 个回复

  • yunxiquzao
    在matlab中,用小波实现语音去噪。噪声为正态随机噪声(In matlab, the Speech Denoising by Wavelet. Noise for the normal random noise)
    2008-05-16 20:51:54下载
    积分:1
  • linear_conv
    linearconvolution with matlab
    2010-12-14 04:02:38下载
    积分:1
  • NIPALS
    function calculates matrices of scores and loadings using NIPALS algorithm (Nonlinear Iterative Partial Least Squares)
    2010-02-03 04:08:45下载
    积分:1
  • matlab-study
    matlab自学通,自己学习matlab的好资料。帮助您一步一步深入matlab的知识世界里(matlab ,it can revolosion the problem about chath the rightes answer .)
    2010-05-22 10:40:08下载
    积分:1
  • capacity_rician
    multiple input multiple output capacity
    2011-06-21 23:43:51下载
    积分:1
  • turbo-source
    turbo码的编译码实现,经典程序。工具:matlab(turbo codes‘s coding and decoding )
    2020-12-09 11:39:19下载
    积分:1
  • puncture
    puncturing simulation in viterbi algorithm in matlab code
    2011-09-06 18:50:43下载
    积分:1
  • calculator
    这是一款很简单计算器,可以帮助人们计算简单的算法(This is a simple calculator, can calculate the simple algorithm )
    2011-10-31 20:55:16下载
    积分:1
  • Project
    (信号与系统实验GUI)本软件针对信号与系统这门课的特点,以信号分析和系统分析作为展示课程内容的主线编写而。信号分析和系统分析两个模块包含了丰富的功能可供使用者参考。((Signal and System Experiment GUI) The software for signal and system characteristics of this course to signal analysis and system analysis as a showcase for the preparation and main course content. Signal analysis and system analysis two modules contain a wealth of features for users' reference.)
    2013-10-19 17:14:52下载
    积分:1
  • recog_rate
    用于matlab的pca,程序规整。便于理解,思路清晰。(for matlab of pca)
    2014-05-06 17:57:21下载
    积分:1
  • 696524资源总数
  • 103848会员总数
  • 55今日下载