An implementation of Exact Soft Confidence-Weighted Learning

## The algorithm

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

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.

## Usage

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.

## Note

1. This package performs only binary classification, not multiclass classification.

2. Training labels must be 1 or -1. No other labels allowed.

## Project details

### Source Distribution

scw-1.1.2.tar.gz (2.4 kB view hashes)

Uploaded source