2 research outputs found
On Extending Neural Networks with Loss Ensembles for Text Classification
Ensemble techniques are powerful approaches that combine several weak
learners to build a stronger one. As a meta learning framework, ensemble
techniques can easily be applied to many machine learning techniques. In this
paper we propose a neural network extended with an ensemble loss function for
text classification. The weight of each weak loss function is tuned within the
training phase through the gradient propagation optimization method of the
neural network. The approach is evaluated on several text classification
datasets. We also evaluate its performance in various environments with several
degrees of label noise. Experimental results indicate an improvement of the
results and strong resilience against label noise in comparison with other
methods.Comment: 5 pages, 5 tables, 1 figure. Camera-ready submitted to The 2017
Australasian Language Technology Association Workshop (ALTA 2017
RELF: Robust Regression Extended with Ensemble Loss Function
Ensemble techniques are powerful approaches that combine several weak
learners to build a stronger one. As a meta-learning framework, ensemble
techniques can easily be applied to many machine learning methods. Inspired by
ensemble techniques, in this paper we propose an ensemble loss functions
applied to a simple regressor. We then propose a half-quadratic learning
algorithm in order to find the parameter of the regressor and the optimal
weights associated with each loss function. Moreover, we show that our proposed
loss function is robust in noisy environments. For a particular class of loss
functions, we show that our proposed ensemble loss function is Bayes consistent
and robust. Experimental evaluations on several datasets demonstrate that our
proposed ensemble loss function significantly improves the performance of a
simple regressor in comparison with state-of-the-art methods.Comment: 18 Pages, 7 figures, Accepted in Applied Intelligence- Springer The
International Journal of Research on Intelligent Systems for Real Life
Complex Problem