Data abundance along with scarcity of machine learning experts and domain specialists necessitates progressive automation of end-to-end machine learning workflows.
In this work, we construct an end-to-end scene recognition pipeline consisting of feature extraction, encoding, pooling and classification.
The crux of the problem in KDD Cup 2016 involves developing data mining techniques to rank research institutions based on publications.
Communication Service Providers (CSPs) are in a unique position to utilize their vast transactional data assets generated from interactions of subscribers with network elements as well as with other subscribers.
While computing similarity between users, we make use of a combined similarity measure involving rating overlap as well as similarity in the latent topic space.