Towards Big Neural Networks: Analysis of Deep Learning Techniques on Diabetes Prediction


Towards Big Neural Networks: Analysis of Deep Learning Techniques on Diabetes Prediction – The majority of tasks in artificial life (including medical data) require the prediction of individual biomarkers for the specific test (e.g. blood pressure or blood glucose) to be considered. However, even though many biomarkers are proposed, current biomarker research deals with only single test. As long as the knowledge of biomarker classification and classification is shared amongst all the test subjects, it has a much higher potential to improve the performance of our system. The best-studied (i.e. the best) biomarker classifier was the one based on genetic algorithm based on gene expression. In this paper, we propose to utilize a genetic algorithm for the purpose of developing a biomarker classifier with the potential of reducing the overall time it takes for the agent to make a decision to classify its samples.

In this paper, we study the problem of online regularization for a deep learning based neural network. As a practical example of the topic of learning with online regularization approaches, we show that a simple CNN CNN architecture (or any CNN structure) can be used to train and learn online regularization based on reinforcement learning, the idea being to combine a fully automatic, unsupervised and non-supervised learning methods. We present a novel learning method which can be used as a model for online regularization based on reinforcement learning (RL) in a deep learning approach. This method can be applied to real-world applications, such as personalized medicine and medical data collection, the latter being a common problem in biomedical and medical networks. We demonstrate the effectiveness of learning supervised training using RL and the effectiveness of using RL on a dataset comprised of 30k patient records for diagnosis and treatment decisions based on a recommendation system. Our approach outperforms many state-of-the-art RL methods on all datasets and outperforms them with respect to accuracy, efficiency and overall quality of the data.

Determining Point Process with Convolutional Kernel Networks Using the Dropout Method

Towards Automated Prognostic Methods for Sparse Nonlinear Regression Models

Towards Big Neural Networks: Analysis of Deep Learning Techniques on Diabetes Prediction

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  • COPA: Contrast-Organizing Oriented Programming

    Discrete Supervised Learning: A Machine Learning Approach to Online RegularizationIn this paper, we study the problem of online regularization for a deep learning based neural network. As a practical example of the topic of learning with online regularization approaches, we show that a simple CNN CNN architecture (or any CNN structure) can be used to train and learn online regularization based on reinforcement learning, the idea being to combine a fully automatic, unsupervised and non-supervised learning methods. We present a novel learning method which can be used as a model for online regularization based on reinforcement learning (RL) in a deep learning approach. This method can be applied to real-world applications, such as personalized medicine and medical data collection, the latter being a common problem in biomedical and medical networks. We demonstrate the effectiveness of learning supervised training using RL and the effectiveness of using RL on a dataset comprised of 30k patient records for diagnosis and treatment decisions based on a recommendation system. Our approach outperforms many state-of-the-art RL methods on all datasets and outperforms them with respect to accuracy, efficiency and overall quality of the data.


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