Machine Learning and Deep Learning


Machine Learning and Deep Learning – We propose a novel deep learning approach to image classification. The training of deep generative models for image classification is carried out by using local feature extraction and deep neural networks (DNNs). We trained deep generative models using a dictionary-based representation of the image, and then trained deep generative models using a local dictionary representation for each image segment. We further evaluated an image classification method which uses a dictionary-based representation and local feature extraction to train a deep generative model using both locally discriminative and discriminative features. The proposed approach is compared with other methods based on a discriminative model and learned feature extraction.

The main aim of this paper is to provide a qualitative review of the current state-of-the-art approach to cancer diagnosis. In this work, we review current approaches and highlight what kind of new insights can be derived from them. We will propose our review of existing approaches and provide a comprehensive survey of current clinical models with cancer diagnosis information. For this purpose, we will focus on a particular study that involves a group of cancer patients from a general population setting. The cancer diagnosis is a new paradigm for new research. Our review will be useful for patients with different diagnoses, as well as for new treatment methods and tools for the cancer diagnosis. This paper will present our review of most of the previous work on the current state-of-the-art approaches while focusing on clinical models. This will provide insights towards the evolution of the current cancer treatment framework.

Fast Bayesian Deep Learning

Supervised Hierarchical Clustering Using Transformed LSTM Networks

Machine Learning and Deep Learning

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  • Interpretable Sparse Signal Processing for High-Dimensional Data Analysis

    A Survey and Comparative Analysis of Current Simulation Techniques for Disease Risk Prediction from Cancerous DentsThe main aim of this paper is to provide a qualitative review of the current state-of-the-art approach to cancer diagnosis. In this work, we review current approaches and highlight what kind of new insights can be derived from them. We will propose our review of existing approaches and provide a comprehensive survey of current clinical models with cancer diagnosis information. For this purpose, we will focus on a particular study that involves a group of cancer patients from a general population setting. The cancer diagnosis is a new paradigm for new research. Our review will be useful for patients with different diagnoses, as well as for new treatment methods and tools for the cancer diagnosis. This paper will present our review of most of the previous work on the current state-of-the-art approaches while focusing on clinical models. This will provide insights towards the evolution of the current cancer treatment framework.


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