Gradient Boosting Machine (GBM) is an extremely powerful supervised learning algorithm that is widely used in practice. GBM routinely features as a leading algorithm in machine learning competitions such as Kaggle and the KDDCup. In this work, we propose Accelerated Gradient Boosting Machine (AGBM) by incorporating Nes…
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4 results for “pseudo-residuals”
Wasserstein gradient boosting predicts probability distributions for supervised learning.
problem Distribution-valued supervised learning where outputs are probability distributions.
method Fits a new weak learner to Wasserstein gradients of loss functionals of probability distributions.
result Superior performance in probabilistic prediction compared to existing methods.
A novel gradient boosting method trains neural networks sequentially for improved performance.
problem Training neural networks efficiently and adapting to different classification speeds.
method Sequential training of neural networks with gradient boosting, where the network is trained in T steps, and a portion of the network is trained at each step to approximate pseudo-residuals.
result The proposed method achieves competitive generalization performance and allows for dynamic adaptation of the model's complexity.
Stochastic gradient descent (SGD) has been the dominant optimization method for training deep neural networks due to its many desirable properties. One of the more remarkable and least understood quality of SGD is that it generalizes relatively well on unseen data even when the neural network has millions of parameters…