A Comparative Analysis of Support Vector Machines – We present a principled alternative to the conventional hardware approach to non-convex and semi-supervised non-parametric classification using deep neural networks (DNNs). In contrast to prior approaches, the DNN formulation can be directly modeled by a matrix and an unaligned matrix. Hence, we provide a principled framework for embedding DNN models in the model space through convolutional neural networks (CNNs). Such an approach is also applicable to general-purpose classification tasks in which CNNs are used as a proxy for the data of the target classification task. We show that this framework is applicable to unsupervised and supervised learning tasks, and demonstrate its superior performance in various instances. We further provide an empirical evaluation demonstrating the effectiveness of our approach for supervised and unsupervised classification tasks.
We present a novel methodology for automatic detection and identification of street scenes from videos. It can scale to millions of frames in real time without requiring human input. We use an adaptive tracking mechanism to automatically classify the scenes in the video, and our method is not subject to human visual cues. We show that a human can accurately identify certain types of street scenes, but it is not the same in every scenario. We present a method for street detection from video that is based on the assumption that the human can not accurately infer the spatial location of the street. Our approach is demonstrated on several car detection benchmarks in a car simulator. The effectiveness of our approach is confirmed by comparing the results to the human detection task on different datasets.
End-to-end Fast Fourier Descriptors for Signal Authentication with Non-Coherent Points
A Multi-Task Algorithm for Predicting Player Profiles and their Predictions from Social Media
A Comparative Analysis of Support Vector Machines
An Implementation of the Random Forests Technique for the Off-Road EnvironmentWe present a novel methodology for automatic detection and identification of street scenes from videos. It can scale to millions of frames in real time without requiring human input. We use an adaptive tracking mechanism to automatically classify the scenes in the video, and our method is not subject to human visual cues. We show that a human can accurately identify certain types of street scenes, but it is not the same in every scenario. We present a method for street detection from video that is based on the assumption that the human can not accurately infer the spatial location of the street. Our approach is demonstrated on several car detection benchmarks in a car simulator. The effectiveness of our approach is confirmed by comparing the results to the human detection task on different datasets.