Training a Neural Network for Detection of Road Traffic Flowchart – A method to predict a traffic event from a prediction of a traffic flowchart is presented here. In addition, we present a model that utilizes the predictions of a few traffic event instances to estimate the expected outcome and perform a prediction that is consistent with the traffic flows. The prediction is learned from the event instances and the prediction is used to optimize a decision tree with a desired outcome. The proposed method utilizes an appropriate distance metric for decision trees trained on street scene data to make it more accurate. The prediction is made from the data extracted from a pedestrian traffic flow chart and the results are compared with the prediction with the road traffic data obtained from a vehicle traffic chart. Experiments show that the learning performance is comparable to two-way street traffic prediction (two-way) in both scenarios. The proposed method demonstrates the usefulness of distance metric for traffic prediction.
We present a novel approach to automatic segmentation of the human brain using deep convolutional neural networks (CNN). The CNN provides a global representation for the input to a CNN that can learn to infer an unknown target part of the visual system. We provide a theoretical study on the CNN and evaluate its performance using the MNIST Dataset. Our result shows that CNNs outperform supervised CNNs. The proposed CNN achieves about 98.6% accuracy in a test set of 8 subjects with an accuracy of 70.5%. The CNN achieves a near 98.5% performance on the MNIST dataset.
Mixed-Membership Stochastic Blockmodular Learning
Toward More Efficient Training of Visual Inspection Cameras
Training a Neural Network for Detection of Road Traffic Flowchart
Efficient Learning-Invariant Signals and Sparse Approximation Algorithms
Unsupervised Learning with Convolutional Neural NetworksWe present a novel approach to automatic segmentation of the human brain using deep convolutional neural networks (CNN). The CNN provides a global representation for the input to a CNN that can learn to infer an unknown target part of the visual system. We provide a theoretical study on the CNN and evaluate its performance using the MNIST Dataset. Our result shows that CNNs outperform supervised CNNs. The proposed CNN achieves about 98.6% accuracy in a test set of 8 subjects with an accuracy of 70.5%. The CNN achieves a near 98.5% performance on the MNIST dataset.