  Deep learning has demonstrated the power of detailed modeling of complex
high-order (multivariate) interactions in data. For some learning tasks there
is power in learning models that are not only Deep but also Broad. By Broad, we
mean models that incorporate evidence from large numbers of features. This is
of especial value in applications where many different features and
combinations of features all carry small amounts of information about the
class. The most accurate models will integrate all that information. In this
paper, we propose an algorithm for Deep Broad Learning called DBL. The proposed
algorithm has a tunable parameter $n$, that specifies the depth of the model.
It provides straightforward paths towards out-of-core learning for large data.
We demonstrate that DBL learns models from large quantities of data with
accuracy that is highly competitive with the state-of-the-art.
