Name
Abstract | ||
|---|---|---|
In multi-view regression the information from multiple representations of the input data is combined to improve the prediction. Inspired by the success of deep learning, this paper proposes a novel model called Weighted Multi-view Deep Neural Networks (MV-DNN) regression. The objective function used is a weighted version of the primal formulation of the existing Multi-View Least Squares Support Vector Machines method, where both the objectives from all different views, as well as the coupling term, are weighted. This work is motivated by the challenging application of weather forecasting. To predict the temperature, the weather variables from several previous days are taken into account. Each feature vector belonging to a previous day (delay) is regarded as a different view. Experimental results on the minimum and maximum temperature prediction in Brussels, reveal the merit of the weighting and show promising results when compared to existing the state-of-the-art methods in weather prediction. |
Year | DOI | Venue |
|---|---|---|
2018 | 10.1007/978-3-030-01424-7_48 | ARTIFICIAL NEURAL NETWORKS AND MACHINE LEARNING - ICANN 2018, PT III |
Keywords | Field | DocType |
Multi-view learning, Neural networks, Deep learning, Weather forecasting | Least squares,Feature vector,Weighting,Regression,Computer science,Support vector machine,Artificial intelligence,Deep learning,Artificial neural network,Weather forecasting,Machine learning | Conference |
Volume | ISSN | Citations |
11141 | 0302-9743 | 0 |
PageRank | References | Authors |
0.34 | 8 | 3 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Zahra Karevan | 1 | 7 | 2.56 |
| Lynn Houthuys | 2 | 16 | 2.60 |
| J. A. Suykens | 3 | 30 | 5.97 |