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.

While recent literature has addressed the problem of graph-based optimization of hierarchical networks, the most relevant applications typically involve optimization of a stochastic optimization problem on a small set of clusters. The problem is often assumed to be intractable and has attracted considerable attention in the past decade. In this work, we investigate the problem of constructing a Markov algorithm that performs sparse linear regression on a large dataset of graphs with a number of nodes that differ only by a small degree. Our algorithm first constructs a partition that is similar if not identical, then splits the partition into a set of nodes that are similar to the same set of nodes. We then use the partition to form a hierarchical structure that is a Gaussian mixture whose structure is the model’s latent space. We use the hierarchical structure as a test that characterizes the expected search space, and show that our algorithm is optimal on a wide set of examples ranging in size from large to small.

The Role of Information Fusion and Transfer in Learning and Teaching Evolution

Learning time, recurrence, and retention in recurrent neural networks

# Machine Learning Methods for Multi-Step Traffic Acquisition

Efficient Representation Learning for Classification

Scalable Generalized Stochastic Graphical ModelsWhile recent literature has addressed the problem of graph-based optimization of hierarchical networks, the most relevant applications typically involve optimization of a stochastic optimization problem on a small set of clusters. The problem is often assumed to be intractable and has attracted considerable attention in the past decade. In this work, we investigate the problem of constructing a Markov algorithm that performs sparse linear regression on a large dataset of graphs with a number of nodes that differ only by a small degree. Our algorithm first constructs a partition that is similar if not identical, then splits the partition into a set of nodes that are similar to the same set of nodes. We then use the partition to form a hierarchical structure that is a Gaussian mixture whose structure is the model’s latent space. We use the hierarchical structure as a test that characterizes the expected search space, and show that our algorithm is optimal on a wide set of examples ranging in size from large to small.

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