501 citations · 511 across the 6 of their papers we have counts for
7 papers
Reinforcement Learning Based Handwritten Digit Recognition with Two-State Q-Learning
Abdul Mueed Hafiz, Ghulam Mohiuddin Bhat
We present a simple yet efficient Hybrid Classifier based on Deep Learning and Reinforcement Learning. Q-Learning is used with two Q-states and four actions. Conventional technique…
Deep Q-Network Based Multi-agent Reinforcement Learning with Binary Action Agents
Abdul Mueed Hafiz, Ghulam Mohiuddin Bhat
Deep Q-Network (DQN) based multi-agent systems (MAS) for reinforcement learning (RL) use various schemes where in the agents have to learn and communicate. The learning is however…
Deep Network Ensemble Learning applied to Image Classification using CNN Trees
Abdul Mueed Hafiz, Ghulam Mohiuddin Bhat
Traditional machine learning approaches may fail to perform satisfactorily when dealing with complex data. In this context, the importance of data mining evolves w.r.t. building an…
Fast Training of Deep Networks with One-Class CNNs
Abdul Mueed Hafiz, Ghulam Mohiuddin Bhat
One-class CNNs have shown promise in novelty detection. However, very less work has been done on extending them to multiclass classification. The proposed approach is a viable effo…
Image Classification by Reinforcement Learning with Two-State Q-Learning
Abdul Mueed Hafiz
In this paper, a simple and efficient Hybrid Classifier is presented which is based on deep learning and reinforcement learning. Here, Q-Learning has been used with two states and…
Digit Image Recognition Using an Ensemble of One-Versus-All Deep Network Classifiers
Abdul Mueed Hafiz, Mahmoud Hassaballah
In multiclass deep network classifiers, the burden of classifying samples of different classes is put on a single classifier. As the result the optimum classification accuracy is n…