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abclass 0.4.1.9010

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.