The following pages link to Composite binary losses (Q2896150):
Displaying 17 items.
- Classification with asymmetric label noise: consistency and maximal denoising (Q315419) (← links)
- The risk of trivial solutions in bipartite top ranking (Q669314) (← links)
- A Fisher consistent multiclass loss function with variable margin on positive examples (Q887268) (← links)
- Calibrated asymmetric surrogate losses (Q1950846) (← links)
- Maximum likelihood degree of variance component models (Q1950847) (← links)
- Properization: constructing proper scoring rules via Bayes acts (Q2183761) (← links)
- Goal scoring, coherent loss and applications to machine learning (Q2191765) (← links)
- Surrogate regret bounds for generalized classification performance metrics (Q2398092) (← links)
- An improved multiclass LogitBoost using adaptive-one-vs-one (Q2514757) (← links)
- How to compare different loss functions and their risks (Q2642921) (← links)
- Convex calibration dimension for multiclass loss matrices (Q2810779) (← links)
- (Q4633014) (← links)
- (Q4969074) (← links)
- (Q4969141) (← links)
- (Q5214229) (← links)
- Learning Optimized Risk Scores (Q5214243) (← links)
- Robust Support Vector Machines for Classification with Nonconvex and Smooth Losses (Q5380444) (← links)
