275 citations · 345 across the 6 of their papers we have counts for
6 papers · 1 filter
On the Calibration of Nested Dichotomies for Large Multiclass Tasks
Tim Leathart, Eibe Frank, Bernhard Pfahringer +1
Nested dichotomies are used as a method of transforming a multiclass classification problem into a series of binary problems. A tree structure is induced that recursively splits th…
Probability Calibration Trees
Tim Leathart, Eibe Frank, Geoffrey Holmes +1
Obtaining accurate and well calibrated probability estimates from classifiers is useful in many applications, for example, when minimising the expected cost of classifications. Exi…
Ensembles of Nested Dichotomies with Multiple Subset Evaluation
Tim Leathart, Eibe Frank, Bernhard Pfahringer +1
A system of nested dichotomies is a method of decomposing a multi-class problem into a collection of binary problems. Such a system recursively applies binary splits to divide the…
XGBoost: Scalable GPU Accelerated Learning
Rory Mitchell, Andrey Adinets, Thejaswi Rao +1
We describe the multi-GPU gradient boosting algorithm implemented in the XGBoost library (https://github.com/dmlc/xgboost). Our algorithm allows fast, scalable training on multi-GP…
MaxGain: Regularisation of Neural Networks by Constraining Activation Magnitudes
Henry Gouk, Bernhard Pfahringer, Eibe Frank +1
Effective regularisation of neural networks is essential to combat overfitting due to the large number of parameters involved. We present an empirical analogue to the Lipschitz con…
Regularisation of Neural Networks by Enforcing Lipschitz Continuity
Henry Gouk, Eibe Frank, Bernhard Pfahringer +1
We investigate the effect of explicitly enforcing the Lipschitz continuity of neural networks with respect to their inputs. To this end, we provide a simple technique for computing…