2 citations · 3 across the 3 of their papers we have counts for
4 papers
Stochastic Gradient Trees
Henry Gouk, Bernhard Pfahringer, Eibe Frank
We present an algorithm for learning decision trees using stochastic gradient information as the source of supervision. In contrast to previous approaches to gradient-based tree le…
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…
Fast Metric Learning For Deep Neural Networks
Henry Gouk, Bernhard Pfahringer, Michael Cree
Similarity metrics are a core component of many information retrieval and machine learning systems. In this work we propose a method capable of learning a similarity metric from da…