Deep neural networks have enhanced the performance of decision making systems in many applications, including image understanding, and further gains can be achieved by constructing ensembles. However, designing an ensemble deep is often not very beneficial since time needed to train generally high or gain obtained significant. In this paper, we analyse error correcting output coding (ECOC) fram...

Deep neural networks (DNNs) have achieved the state of art performance in numerous fields. However, DNNs need high computation times, and people always expect better a lower computation. Therefore, we study human somatosensory system design network (SpinalNet) to achieve higher accuracy with fewer computations. Hidden layers traditional NNs receive inputs previous layer, apply activation functi...

A Deep Neural Sentiment Classification Network (DNSCN) is developed in this work to classify the Twitter data unambiguously. It attempts extract negative and positive sentiments database. The main goal of system find sentiment behavior tweets with minimum ambiguity. well-defined neural network extracts deep features from automatically. Before extracting deeper deeper, text each tweet represente...

Neuromorphic hardware tends to pose limits on the connectivity of deep networks that one can run on them. But also generic hardware and software implementations of deep learning run more efficiently on sparse networks. Several methods exist for pruning connections of a neural network after it was trained without connectivity constraints. We present an algorithm, DEEP R, that enables us to train...