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abclass 0.5.0

Major changes

• Simplified specification of group penalty via abclass.control().

Minor changes

• Changed the default alignment to lambda for cv.abclass() and refit in et.abclass() if a sequence of lambda’s is specified. A warning message would be thrown out for the former.

abclass 0.4.0

New features

• Added support of sparse matrix x of class sparseMatrix (provided by the {Matrix} package) for abclass() and predict.abclass().
• Added new functions named cv.abclass() and et.abclass() for training and tuning the angle-based classifiers with cross-validation and an efficient tuning procedure for lasso-type algorithms, respectively. See the corresponding function documentation for details.
• Added experimental classifiers with sup-norm penalties. See the functions supclass() and cv.supclass() for details.

Major Changes

• Simplified the function abclass() and moved the tuning procedure by cross-validation to the function cv.abclass().

Minor Changes

• Changed the default values of the following arguments for abclass.control().
  • alpha: from 0.5 to 1.0
  • epsilon: from 1e-3 to 1e-4

Bug fixes

• Fixed alignment in abclass.control().

abclass 0.3.0

New features

• Added experimental group-wise regularization by group SCAD and group MCP penalty.
• Added a new function named abclass.control() to specify the control parameters and simplify the main function interface.

Minor changes

• Renamed the argument max_iter to maxit for abclass().

Bug fixes

• Fixed the validation indices in the cross-validation procedure

abclass 0.2.0

New features

• Added experimental group-wise regularization by group lasso penalty.

Minor changes

• Removed the function call from the return of abclass() to avoid unnecessarily large returned objects
• Changed the default value of lum_c for abclass() from 0 to 1.
• Renamed the argument rel_tol to epsilon for abclass().

Bug fixes

• Fixed the first derivatives of the boosting loss
• Fixed the label prediction by using the fitted inner products instead of the probability estimates
• Fixed the computation of regularization terms for verbose outputs in AbclassNet
• Fixed the computation of validation accuracy in cross-validation
• Fixed the assignment of lum_c in the associated header files.
• Fixed the computation of lower bound for distinct observation weights

abclass 0.1.0

New features

• The first release of abclass providing the multi-category angle-based large-margin classifiers with various loss functions.

These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.