Reinforcement based Multi-Model Deep Learning Algorithm for Classification
Thella Sunitha1, G. Lavanya Devi2

1Thella Sunitha*, Department of CSE, Andhra University, Visakhapatnam India.
2Dr. G. Lavanya Devi, Department of CSE, Andhra University, Visakhapatnam India.
Manuscript received on November 12, 2019. | Revised Manuscript received on 23 November, 2019. | Manuscript published on December 10, 2019. | PP: 1704-1710 | Volume-9 Issue-2, December 2019. | Retrieval Number: B6465129219/2019©BEIESP | DOI: 10.35940/ijitee.B6465.129219
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Abstract: Nowadays researchers are focused on processing the multi-media data for classifying the queries of end users by using search engines. The hybrid combination of a powerful classifier and deep feature extractor are used to develop a robust model, which is performed in a high dimensional space. In this research, a three different types of algorithms are combined to attain a stochastic belief space policy, where these algorithms include generative adversary modelling, maximum entropy Reinforcement Learning (RL) and belief space planning which leads to develop a multi-model classification algorithm. In the simulation framework, different adversarial behaviours are used to minimize the agent’s action predictability, which has resulted the proposed method to attain robustness, while comparing with unmodelled adversarial strategies. The proposed reinforcement based Deep Learning (DL) algorithm can be used as multi-model classification purpose. The single neural network algorithm can perform the classification on text data and image data. The RL learns the appropriate belief space policy from the feature extracted information of the text and image data, the belief space policy is generated based on the maximum entropy computation. 
Keywords: Deep Learning Algorithm, Document Classification, Image Classification, Multi-Model Classification, Reinforcement learning.
Scope of the Article: Classification