Simplified specification of group penalty viaabclass.control().
Minor changes
Changed the default alignment to lambdafor cv.abclass() and refit inet.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 classsparseMatrix (provided by the {Matrix}package) for abclass() andpredict.abclass().
Added new functions named cv.abclass() andet.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 functioncv.abclass().
Minor Changes
Changed the default values of the following arguments forabclass.control().
alpha: from 0.5 to 1.0
epsilon: from 1e-3 to1e-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 forabclass().
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 forabclass() from 0 to 1.
Renamed the argument rel_tol to epsilonfor 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 inAbclassNet
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.