Machine Learning Methods for Multi-Step Traffic Acquisition


Machine Learning Methods for Multi-Step Traffic Acquisition – Sparse-time classification (STR) has emerged as a promising tool for automatic vehicle identification. The main drawback of STR is its lack of training data and the difficulty of handling noisy data. In this work we present an innovative approach to the problem using Convolutional Neural Networks. In our model, we first use unsupervised learning as feature representation for image classification: the Convolutional Neural Network (CNN) is trained with an unlabeled image. The CNN learns a binary metric feature embedding representation of its output vectors (e.g., the k-dimensional). Following this representation, the CNN can model the training data by selecting a high-quality subset of the training data. Our method learns the representations and, by using the learned representations, can be used with the standard segmentation and classification algorithms in order to learn the feature representation for the given dataset. We evaluate our method on the challenging TIDA dataset and compare it to the state-of-the-arts.

As a new technology, deep learning models are becoming popular tools for learning tasks. However, deep learning models are extremely time-consuming to implement, especially for non-Gaussian models. In this paper, a novel deep learning approach, Deep-CNN, is proposed to implement the task of learning visual object from a large and sparse set of input images. Since the task of learning visual object is not a common task of visual system, Deep-CNN model performs extremely fast and is also the best model to implement for learning the object recognition task. The proposed model is built on top of the existing state-of-the-art supervised learning method which does not require any training dataset. We show that Deep CNN model is state-of-the-art on two benchmark tasks: object recognition and word recognition.

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Machine Learning Methods for Multi-Step Traffic Acquisition

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  • Practical Approach to Neural Network-Based Human Action Recognition

    Stochastic Optimization for Deep Neural NetworksAs a new technology, deep learning models are becoming popular tools for learning tasks. However, deep learning models are extremely time-consuming to implement, especially for non-Gaussian models. In this paper, a novel deep learning approach, Deep-CNN, is proposed to implement the task of learning visual object from a large and sparse set of input images. Since the task of learning visual object is not a common task of visual system, Deep-CNN model performs extremely fast and is also the best model to implement for learning the object recognition task. The proposed model is built on top of the existing state-of-the-art supervised learning method which does not require any training dataset. We show that Deep CNN model is state-of-the-art on two benchmark tasks: object recognition and word recognition.


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