A Survey of Feature Selection Methods in Deep Neural Networks – Deep learning, a technology which uses features as inputs to learn models, has been an open research area. Despite many attempts to use feature selection methods to make deep learning as a tool for machine learning, most of these work have focused on feature selection using two-part prediction or machine learning methods. While the two-part methods are successful for feature selection, they focus on the classification task and not on the real world. In this paper we propose a novel machine learning approach which combines the two-part prediction and classification processes to produce feature selections. The model can predict the feature set and the prediction process is conducted in a supervised fashion while learning the model. Our proposed algorithm uses a convolutional neural network to learn the classification task while the feature selection process is conducted in a supervised fashion. The proposed algorithm achieves an accuracy of 99.8% for the classification task and an accuracy of 99.8% for the real world task.
We study the feasibility and safety of an automated system for verifying, protecting, and monitoring the integrity of user records in online social networks. We propose a novel online verification system that is fully automated on-line. To the best of our knowledge, this is the first such system for verifying and monitoring user records. We show that the system can be used to verify all records in a given database and is capable of safely and securely evaluating the identity of users in online social networks of the community.
Using Deep Learning and Deep Convolutional Neural Networks For Deformable Object Recognition
Predicting behavior of a child by exploiting context information in reinforcement learning networks
A Survey of Feature Selection Methods in Deep Neural Networks
Neural Embeddings for Sentiment Classification
A Novel Approach to Evaluating the Security of Database Users via an Impromptu Distributed Denial of Service AttackWe study the feasibility and safety of an automated system for verifying, protecting, and monitoring the integrity of user records in online social networks. We propose a novel online verification system that is fully automated on-line. To the best of our knowledge, this is the first such system for verifying and monitoring user records. We show that the system can be used to verify all records in a given database and is capable of safely and securely evaluating the identity of users in online social networks of the community.