R: Multiple-Instance Learning with Support Vector Machines

# Multiple-Instance Learning with Support Vector Machines

## Documentation for package ‘mildsvm’ version 0.4.1

- DESCRIPTION file
- Package NEWS
- Package README

## Help Pages

as_mild_df | Coerce to MILD data frame
as_mi_df | Coerce to MI data frame
bag_instance_sampling | Sample 'mild_df' object by bags and instances
build_fm | Build a feature map on new data
build_fm.kfm_exact | Build a feature map on new data
build_fm.kfm_nystrom | Build a feature map on new data
build_instance_feature | Flatten 'mild_df' data to the instance level
classify_bags | Classify y from bags
cv_misvm | Fit MI-SVM model to the data using cross-validation
cv_misvm.default | Fit MI-SVM model to the data using cross-validation
cv_misvm.formula | Fit MI-SVM model to the data using cross-validation
cv_misvm.mi_df | Fit MI-SVM model to the data using cross-validation
formatting | Printing multiple instance data frames
generate_mild_df | Generate mild_df using multivariate t and normal distributions.
kfm_exact | Create an exact kernel feature map
kfm_nystrom | Fit a Nyström kernel feature map approximation
kfm_nystrom.default | Fit a Nyström kernel feature map approximation
kfm_nystrom.mild_df | Fit a Nyström kernel feature map approximation
kme | Calculate the kernel mean embedding matrix
kme.default | Calculate the kernel mean embedding matrix
kme.mild_df | Calculate the kernel mean embedding matrix
mi | Create an 'mi' object
mild | Create a mild object
mild_df | Build a MILD data frame
mior | Fit MIOR model to the data
mior.default | Fit MIOR model to the data
mior.formula | Fit MIOR model to the data
mior.mi_df | Fit MIOR model to the data
mismm | Fit MILD-SVM model to the data
mismm.default | Fit MILD-SVM model to the data
mismm.formula | Fit MILD-SVM model to the data
mismm.mild_df | Fit MILD-SVM model to the data
misvm | Fit MI-SVM model to the data
misvm.default | Fit MI-SVM model to the data
misvm.formula | Fit MI-SVM model to the data
misvm.mild_df | Fit MI-SVM model to the data
misvm.mi_df | Fit MI-SVM model to the data
misvm_orova | Fit MI-SVM model to ordinal outcome data using One-vs-All
misvm_orova.default | Fit MI-SVM model to ordinal outcome data using One-vs-All
misvm_orova.formula | Fit MI-SVM model to ordinal outcome data using One-vs-All
misvm_orova.mi_df | Fit MI-SVM model to ordinal outcome data using One-vs-All
mi_df | Build a multiple instance (MI) data frame
omisvm | Fit MI-SVM-OR model to ordinal outcome data
omisvm.default | Fit MI-SVM-OR model to ordinal outcome data
omisvm.formula | Fit MI-SVM-OR model to ordinal outcome data
omisvm.mi_df | Fit MI-SVM-OR model to ordinal outcome data
ordmvnorm | Sample ordinal MIL data using mvnorm
predict.cv_misvm | Predict method for 'cv_misvm' object
predict.mior | Predict method for 'mior' object
predict.mismm | Predict method for 'mismm' object
predict.misvm | Predict method for 'misvm' object
predict.misvm_orova | Predict method for 'misvm_orova' object
predict.omisvm | Predict method for 'omisvm' object
predict.smm | Predict method for 'smm' object
predict.svor_exc | Predict method for 'svor_exc' object
print.mild_df | Printing multiple instance data frames
print.mi_df | Printing multiple instance data frames
smm | Fit SMM model to the data
smm.default | Fit SMM model to the data
smm.formula | Fit SMM model to the data
smm.mild_df | Fit SMM model to the data
summarize_samples | Summarize data across functions
summarize_samples.default | Summarize data across functions
summarize_samples.mild_df | Summarize data across functions
svor_exc | Fit SVOR-EXC model to ordinal outcome data
svor_exc.default | Fit SVOR-EXC model to ordinal outcome data
svor_exc.formula | Fit SVOR-EXC model to ordinal outcome data
svor_exc.mi_df | Fit SVOR-EXC model to ordinal outcome data