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 1.0epsilon: 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.