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| Dataset & Software |
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[ 2007-9-24 13:20:00 | By: Byron ] |
The code or data listed below were developed or collected by LAMDA members. They are shared here for expediating the communication of research results among scientific communities. They can be freely used at your own risk, given that the contributions of LAMDA are appropriatedly cited or acknowledged in your publications.
Note: They can only be used for academic usage. For other purposes, please contact with Prof. Zhi-Hua Zhou.
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Data |
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MILWEBData for Multi-Instance Learning Based Web Index Recommendation.
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Code/Demo |
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BPMLLBPMLL is a package for training multi-label BP neural networks. The package includes the MATLAB code of the algorithm BP-MLL, which is designed to deal with multi-label learning. It is in particular useful when a real-world object is associated with multiple labels simultaneously.
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C4.5Rule-PANEC4.5Rule-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.
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CCECCE is a multi-instance learning method solving multi-instance problems through adapting multi-instance representation to single-instance algorithms, which is quite different from existing multi-instance learning algorithms which attempt to adapt single-instance algorithms to multi-instance representation.
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ClustererEnsembleClustererEnsemble is a package containing methods for building ensembles of clusterers. In particular, ensembles of k -means clusterings are constructed with voting, weighted voting, selective voting, and selective weighted voting.
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CSNNThis package contains 6 algorithms for training cost-sensitive neural networks. They are over-sampling, under-sampling, threshold-moving, SMOTE and two ensemble methods, i.e. hard-ensemble and soft-ensemble.
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Demo of FANNC and FANREFANNC is a fast neural classifier, and FANRE is a fast neural regressor. Both are developed based on Adaptive Resonance Theory and Field Theory. Prominent characteristics of these neural networks mainly include: they do not require the user to setup the number of hidden units; they only scan the training set once; they are incremental learning algorithms that can be used in online learning environments; etc.
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Demo of Wu&Zhou's Face DetectorWu&Zhou-FaceDetector is a demo for an efficient face candidates selector proposed for face detection tasks in still gray-level images.
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FASBIRFASBIR is a variant of Bagging algorithm, whose purpose is to improve accuracy of local learners, such as kNN, through multi-model perturbing ensemble.
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GASENGASEN is a selective ensemble method using genetic algorithm to help select a subset of neural networks (or other learners, with appropriate modification) to compose an ensemble, which is better than directly ensembling all the neural networks available.
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MIL-EnsembleThis 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.
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MIMLBOOST & MIMLSVMThe package includes the MATLAB code of algorithms MIMLBOOST and MIMLSVM , both of which are designed to deal with multi-instance multi-label learning. It is in particular useful when a real-world object is associated with multiple instances as well as multiple labels simultaneously.
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NeC4.5NeC4.5 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.
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RBFMIPRBFMIP is a package for training multi-instance RBF neural networks.
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S-ISOMAP S-ISOMAP is a manifold learning algorithm, which is a supervised variant of ISOMAP.
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TriTrain TriTrain is a semi-supervised algorithm, which iteratively refines each of the three component classifiers generated from the original labeled example set with the unlabeled examples based on the predictions the other classifiers agree on, and finally combines their prediction via majority voting.
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