MIL-Ensemble
所属分类:人工智能/神经网络/深度学习
开发工具:matlab
文件大小:3953KB
下载次数:151
上传日期:2008-10-16 21:44:52
上 传 者:
阳台
说明: 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
文件列表:
MIL-Ensemble (0, 2004-06-18)
MIL-Ensemble\Data Preparation (0, 2004-06-18)
MIL-Ensemble\Data Preparation\10-fold cross-validation (0, 2004-06-18)
MIL-Ensemble\Data Preparation\10-fold cross-validation\divide_10fold_Musk1.m (4795, 2004-01-14)
MIL-Ensemble\Data Preparation\10-fold cross-validation\divide_10fold_Musk2.m (4808, 2004-01-14)
MIL-Ensemble\Data Preparation\musk data from UCI ML Repository (0, 2004-06-18)
MIL-Ensemble\Data Preparation\musk data from UCI ML Repository\clean1.data (0, 2004-06-18)
MIL-Ensemble\Data Preparation\musk data from UCI ML Repository\clean1.data\clean1.data (328852, 1994-11-17)
MIL-Ensemble\Data Preparation\musk data from UCI ML Repository\clean1.data.Z (110731, 1994-11-17)
MIL-Ensemble\Data Preparation\musk data from UCI ML Repository\clean1.info (5804, 1994-11-17)
MIL-Ensemble\Data Preparation\musk data from UCI ML Repository\clean1.names (8121, 1994-11-17)
MIL-Ensemble\Data Preparation\musk data from UCI ML Repository\clean2.data (0, 2004-06-18)
MIL-Ensemble\Data Preparation\musk data from UCI ML Repository\clean2.data\clean2.data (4590077, 1994-11-17)
MIL-Ensemble\Data Preparation\musk data from UCI ML Repository\clean2.data.Z (1470557, 1994-11-17)
MIL-Ensemble\Data Preparation\musk data from UCI ML Repository\clean2.info (7192, 1994-11-17)
MIL-Ensemble\Data Preparation\musk data from UCI ML Repository\clean2.names (67816, 1994-11-17)
MIL-Ensemble\Data Preparation\musk data from UCI ML Repository\Index (255, 1996-03-19)
MIL-Ensemble\Data Preparation\Preprocessed data (0, 2004-06-18)
MIL-Ensemble\Data Preparation\Preprocessed data\Musk1 (0, 2004-06-18)
MIL-Ensemble\Data Preparation\Preprocessed data\Musk1\all.txt (318358, 2001-07-17)
MIL-Ensemble\Data Preparation\Preprocessed data\Musk1\molecule_num.TXT (187, 2002-04-22)
MIL-Ensemble\Data Preparation\Preprocessed data\Musk2 (0, 2004-06-18)
MIL-Ensemble\Data Preparation\Preprocessed data\Musk2\all.txt (4428898, 2002-04-04)
MIL-Ensemble\Data Preparation\Preprocessed data\Musk2\molecule_num.TXT (272, 2002-05-07)
MIL-Ensemble\Ensemble Algorithm (0, 2004-06-18)
MIL-Ensemble\Ensemble Algorithm\APR (0, 2004-06-18)
MIL-Ensemble\Ensemble Algorithm\APR\Bagging_APR_Musk1.m (3110, 2004-01-14)
MIL-Ensemble\Ensemble Algorithm\APR\Bagging_APR_Musk2.m (3098, 2004-01-14)
MIL-Ensemble\Ensemble Algorithm\auxiliary function (0, 2004-06-18)
MIL-Ensemble\Ensemble Algorithm\auxiliary function\copy.m (72, 2002-06-12)
MIL-Ensemble\Ensemble Algorithm\C-kNN (0, 2004-06-18)
MIL-Ensemble\Ensemble Algorithm\C-kNN\Bagging_C_kNN_Musk1.m (3383, 2004-01-14)
MIL-Ensemble\Ensemble Algorithm\C-kNN\Bagging_C_kNN_Musk2.m (3383, 2004-01-14)
MIL-Ensemble\Ensemble Algorithm\Diverse Density (0, 2004-06-18)
MIL-Ensemble\Ensemble Algorithm\Diverse Density\Bagging_DD_Musk1.m (3515, 2004-01-14)
MIL-Ensemble\Ensemble Algorithm\Diverse Density\Bagging_DD_Musk2.m (3551, 2004-01-14)
MIL-Ensemble\Ensemble Algorithm\EM-DD (0, 2004-06-18)
MIL-Ensemble\Ensemble Algorithm\EM-DD\Bagging_EMDD_Musk1.m (3746, 2004-01-14)
MIL-Ensemble\Ensemble Algorithm\EM-DD\Bagging_EMDD_Musk2.m (3743, 2004-01-14)
MIL-Ensemble\Individual Algorithm (0, 2004-06-18)
... ...
***************************************************************************
ReadMe for the MIL-Ensemble Package
***************************************************************************
Description: 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.
Reference: Z.-H. Zhou and M.-L. Zhang. Ensembles of multi-instance
learners. In: Proceedings of the 14th European Conference on
Machine Learning (ECML'03), Cavtat-Dubrovnik, Croatia, LNAI
2837, 2003, pp.492-502.
ATTN: This package is free for academic usage. You can run it at
your own risk. For other purposes, please contact Prof.
Zhi-Hua Zhou (zhouzh@nju.edu.cn).
Requirement: To use this package, the MATLAB environment must be available.
ATTN2: This package was developed by Mr. Min-Ling Zhang (zhangml@lamda.
nju.edu.cn). There is a ReadMe file roughly explaining the codes.
But for any problem concerning the code, please feel free to
contact Mr. Zhang.
***************************************************************************
This toolbox contains programs for four different multi-instance learners and
their ensemble versions. In detail, this toolbox contains three parts:
1)Data Preparation
This part includes three components:
1.1) Original Musk data [1] from UCI machine learning repository
1.2) Preprocessed Musk data for further usage
1.3) functions for dividing the Musk data into 10 folds, which are called
before conducting 10-fold cross-validation experiments
2)Individual Algorithm
This part includes programs for four different multi-instance learners:
2.1) Iterated-Discrim APR [1], the main function is 'IDAPR'
2.2) Citation-kNN [2], the main function is 'CKNN'
2.3) Diverse Density [3], the main function is 'maxDD'
2.4) EM-DD [4], the main function is 'EMDD'
For more details of the above algorithms, please refer to the correponding
codes and comments.
3)Ensemble Algorithm
This part includes programs for the Ensemble of four different multi-instance
learners:
3.1) Ensemble of Iterated-Discrim APR
3.2) Ensemble of Citation-kNN
3.3) Ensemble of Diverse Density
3.4) Ensemble of EM-DD
For more details of the functionality of the above algorithms, please refer
to [5] and the corresponding codes and comments.
[1] T.G. Dietterich, R.H. Lathrop, and T. Lozano-Perez. Solving the multiple-
instance problem with axis-parallel rectangles. Artificial Intelligence,
89(1-2): 31-71, 1997.
[2] J. Wang and J.-D. Zucker. Solving the multiple-instance problem: a lazy
learning approach. In: Proceedings of the 17th International Conference on
Machine Learning, San Francisco, CA: Morgan Kaufmann, 1119-1125, 2000.
[3] Maron O. Learning from ambiguity [PhD dissertation]. Department of Electrical
Engineering and Computer Science, MIT, 19***
[4] Q. Zhang and S. A. Goldman. EM-DD: an improved multi-instance learning
technique. In: Advances in Neural Processing Systems 14, Cambridge, MA: MIT
Press, 1073-1080, 2001.
[5] Z.-H. Zhou and M.-L. Zhang. Ensembles of multi-instance learners. In: Lecture
Notes in Computer Science 2837, Berlin: Springer-Verlag, 2003, 492-502.
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