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An implementation of Exact Soft Confidence-Weighted Learning

Project Description

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
  • 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.

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.
Release History

Release History

This version
History Node

1.1.2

History Node

1.1.1

History Node

1.1.0

History Node

1.0

Download Files

Download Files

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File Name & Checksum SHA256 Checksum Help Version File Type Upload Date
scw-1.1.2.tar.gz (2.4 kB) Copy SHA256 Checksum SHA256 Source Feb 22, 2016

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