arXiv:2402.08056
공식 코드 · Papers with Code
MIML library: a Modular and Flexible Library for Multi-instance Multi-label Learning
원문 초록
MIML library is a Java software tool to develop, test, and compare classification algorithms for multi-instance multi-label (MIML) learning. The library includes 43 algorithms and provides a specific format and facilities for data managing and partitioning, holdout and cross-validation methods, standard metrics for performance evaluation, and generation of reports. In addition, algorithms can be executed through $xml$ configuration files without needing to program. It is platform-independent, extensible, free, open-source, and available on GitHub under the GNU General Public License.
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Cut out and Replay: A Simple yet Versatile Strategy for Multi-Label Online Continual Learning
ProtoECGNet: Case-Based Interpretable Deep Learning for Multi-Label ECG Classification with Contrastive Learning
A Simple but Effective Closed-form Solution for Extreme Multi-label Learning
Towards Macro-AUC oriented Imbalanced Multi-Label Continual Learning
Dual-Decoupling Learning and Metric-Adaptive Thresholding for Semi-Supervised Multi-Label Learning
Multi-label Learning with Random Circular Vectors
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