A novel matrix-similarity based loss function for joint regression and classification in AD diagnosis.
Xiaofeng ZhuHeung-Il SukDinggang ShenPublished in: NeuroImage (2014)
Keyphrases
- loss function
- support vector
- hinge loss
- reproducing kernel hilbert space
- gradient boosting
- classification accuracy
- pairwise
- decision trees
- learning to rank
- risk minimization
- logistic regression
- regression model
- pattern classification
- support vector machine svm
- model selection
- convex loss functions
- kernel function
- square loss
- feature space
- bayes rule
- empirical risk
- supervised learning
- regression problems
- feature selection
- training samples
- base learners
- text classification
- multi class
- solution path
- boosting algorithms
- binary classification
- ad patients