53 lines
1.5 KiB
Markdown
53 lines
1.5 KiB
Markdown
# TSVMs
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A collection of Twin Support Vector Machine (TSVM) implementations for pattern classification.
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## Implementations
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- **TSVM** - Twin Support Vector Machine
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- **UTSVM** - Unsupervised Twin Support Vector Machine
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- **NewtonUTSVM** - Newton Method-based Unsupervised Twin Support Vector Machine
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- **DTSVM** - Deep Twin Support Vector Machine
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- **LSTSVM** - Least Squares Twin Support Vector Machine
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- **DLSTSVM** - Deep Least Squares Twin Support Vector Machine
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- **RUTSVM** - Robust Unsupervised Twin Support Vector Machine
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- **RULSTSVM** - Robust Unsupervised Least Squares Twin Support Vector Machine
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## Usage
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Each implementation follows a similar interface:
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```python
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from TSVM import TSVM
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# Prepare your data
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# X: features (n_samples, n_features)
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# y: labels (n_samples, 1) with values +1 or -1
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# C1, C2: regularization parameters
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# Create and train the model
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model = TSVM(X, y, C1=1.0, C2=1.0)
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model.fit()
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# Make predictions
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model.predict(x_test)
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predictions = model.get_preds()
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```
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## Requirements
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- numpy
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- cvxopt
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## References
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- TSVM: [IEEE Paper](https://ieeexplore.ieee.org/document/4135685)
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- UTSVM: [ScienceDirect](https://www.sciencedirect.com/science/article/abs/pii/S0893608012002304)
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- DTSVM: [IEEE Paper](https://ieeexplore.ieee.org/abstract/document/7022580)
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- LSTSVM: [ScienceDirect](https://www.sciencedirect.com/science/article/abs/pii/S0957417408006854)
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- RUTSVM: [ScienceDirect](https://www.sciencedirect.com/science/article/abs/pii/S0031320319304510)
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## Author
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Saeed Khosravi
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