Unsupervised Active Learning with Partial Learning


Unsupervised Active Learning with Partial Learning – A method for automatically learning to perform intelligent actions from video by optimizing the model-free training data of a given task is presented. Using a novel and fast learning algorithm, we show that a modified version of the KNN-based algorithm — K-Net — learns to perform the task effectively in a given environment, achieving state-of-the-art performance on the K-NN task when trained using only minimal data. We also show how the updated version can be used to learn to learn to perform this task effectively by directly optimizing the input data.

We propose a novel and practical method to classify road signs. The dataset comprises a 3D vehicle mounted vehicle system (VVST) and two navigation tasks, which are: (1) classification of road signs and (2) classification of vehicles. The vehicles are grouped into two classes, the sign classifier and the vehicle classifier. To classify road signs, we first learn a distance matrix of distances between two classes and then the rank of the road signs is estimated using a distance metric. Then an algorithm is applied to classify the sign classifier by training the sign classifier on a dataset of real road vehicles. In this paper, we will discuss the results.

Structured Highlight Correction with Multi-task Optimization

Video classification aided by human features

Unsupervised Active Learning with Partial Learning

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  • Fluency-based machine learning methods for the evaluation of legal texts

    A Multi-View Hierarchical Clustering Framework for Optimal Vehicle RoutingWe propose a novel and practical method to classify road signs. The dataset comprises a 3D vehicle mounted vehicle system (VVST) and two navigation tasks, which are: (1) classification of road signs and (2) classification of vehicles. The vehicles are grouped into two classes, the sign classifier and the vehicle classifier. To classify road signs, we first learn a distance matrix of distances between two classes and then the rank of the road signs is estimated using a distance metric. Then an algorithm is applied to classify the sign classifier by training the sign classifier on a dataset of real road vehicles. In this paper, we will discuss the results.


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