This is an online supervised learning algorithm which utilizes all the four salient properties:
- Large margin training
- Confidence weighting
- Capability to handle non-separable data
- Adaptive margin
The paper is here.
SCW has 2 formulations of its algorithm which are SCW-I and SCW-II. They can be accessed like below.
scw.SCW1(C, ETA)
scw.SCW2(C, ETA)
C and ETA are hyperparameters.
Dependent packages
- numpy
- scipy
Install via pip
pip3 install scw
The interface is almost same as the classification models in scikit-learn (e.g. LinearSVC)
from scw import SCW1, SCW2
scw = SCW1(C=1.0, ETA=1.0)
scw.fit(X, y)
y_pred = scw.perdict(X)
X and y are 2-dimensional and 1-dimensional array respectively.
X is a set of data vectors. Each row of X represents a feature vector.
y is a set of labels corresponding with X.
- This package performs only binary classification, not multiclass classification.
- Training labels must be 1 or -1. No other labels allowed.