Ensemble methods are learning algorithms that construct a set of classifiers and then
classify new data points by taking a (weighted) vote of their predictions. The original …

This article reviews five approximate statistical tests for determining whether one learning
algorithm outperforms another on a particular learning task. These test sare compared …

Current deep-learning models are mostly built upon neural networks, ie multiple layers of
parameterized differentiable non-linear modules that can be trained by backpropagation. In …

Recent work has shown that optical flow estimation can be formulated as a supervised
learning task and can be successfully solved with convolutional networks. Training of the so …

Mapping road networks is currently both expensive and labor-intensive. High-resolution
aerial imagery provides a promising avenue to automatically infer a road network. Prior work …

Accurate river streamflow forecasts are a vital tool in the fields of water security, flood
preparation and agriculture, as well as in industry more generally. Traditional physics-based …

An up-to-date, self-contained introduction to a state-of-the-art machine learning approach,
Ensemble Methods: Foundations and Algorithms shows how these accurate methods are …

In this study, a new hybrid model, namely the Electromagnetism-based Firefly Algorithm-
Artificial Neural Network (EFA-ANN), is proposed to forecast the energy consumption in …

The idea of ensemble methodology is to build a predictive model by integrating multiple
models. It is well-known that ensemble methods can be used for improving prediction …

Decision trees have become one of the most powerful and popular approaches in
knowledge discovery and data mining; it is the science of exploring large and complex …