Large-Scale Machine Learning for Classification


Large-Scale Machine Learning for Classification – Many applications with a particular focus on a variety of complex datasets usually require very extensive training samples. In this paper, we focus on a class of data-driven classification problems, where it is challenging to accurately predict the classification results given a data set with a high-dimensional representation of the data. We show how to efficiently learn this representation using Deep Neural Networks (DNNs). We show that such a representation can be used as a basic framework for learning a representation of data as a function of the dimension, i.e., size of the data. We then propose a learning algorithm based on Deep Learning (DL) to learn the representation of data from the representation. Experiments on a large dataset show that the proposed ML method outperforms the state-of-the-art classification methods by a large margin.

We present a novel technique for learning deep machine-learning representations of images by learning a deep model of the network structure, and then applying it to the task of image classification. We show that our deep model is able to achieve better classification performance for images compared to prior state-of-the-art methods. While previous approaches focus on learning from the network structure, our model can handle images from a much larger network structure using only a single learned feature learned from the network images. We show in the literature that our approach can improve classification performance.

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Large-Scale Machine Learning for Classification

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    Learning how to model networksWe present a novel technique for learning deep machine-learning representations of images by learning a deep model of the network structure, and then applying it to the task of image classification. We show that our deep model is able to achieve better classification performance for images compared to prior state-of-the-art methods. While previous approaches focus on learning from the network structure, our model can handle images from a much larger network structure using only a single learned feature learned from the network images. We show in the literature that our approach can improve classification performance.


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