v2.2

• Added support for hybrid-kernel SVDD.
  K =w1K1+w2K2+...+wn*Kn
• Added support for output of optimization results.
• Improved visualization of parameter optimization.
• Fixed minor bugs.

v2.1.5

Update README.md

v2.1.2

Update demo_nSVDD.m

v2.1.1

• Fixed bugs with Display Settings for Parameter Optimization.

v2.1

1. SVDD model for one-class or binary classification
2. Multiple kinds of kernel functions (linear, gaussian, polynomial, sigmoid, laplacian)
3. Visualization of decision boundaries for 2D or 3D data
4. Parameter Optimization using Bayesian optimization, Genetic Algorithm, and Particle Swarm Optimization
5. Weighted SVDD model

v2.0

1. SVDD model for training dataset containing only positive training data. (SVDD)
2. SVDD model for training dataset containing both positive training data and negative training data. (nSVDD)
3. Multiple kinds of kernel functions.
4. Visualization module including ROC curve plotting, testing result plotting, and decision boundary.
5. Dimensionality Reduction (DR) module based on 'drtoolbox'.
6. Parameter Optimization (PO) module based on the 'YPEA' toolbox.
7. Preprocessing module for data standardization or data normalization.

v1.0

Update README.md