users can examine intermediate results of complex or long running computations, without waiting for the computation to complete.However, with progressive visualization still a nascent analysis technique, a para
On the other hand, PHA, as an exact method for convex problems, suffers from the need to iteratively solve numerous sub-problems which are computationally costly. In this paper, we developed two novel algorithms integrating SAA and PHA for solving the CRFLP under uncertainty. The developed ...
Brian: Our data science team that's responsible for the new scoring algorithms and Snapshot is always looking for the next big thing, the next thing that they can use to score driving behavior. So they bring in all sorts of different external datasets, external to our company—wea...
Martin, D., Fowlkes, C., Tal, D., & Malik, J.: A database of human segmented natural images and its application to evaluating segmentation algorithms and measuring ecological statistics. In: Proceedings of the IEEE International Conference on Computer Vision, (2002) Huang, J.B., Singh, A...
This category of supervised learning algorithms is powerful and can automatically learn to identify task-specific features from images during the training stage. Multiple CNN-based methods already exist for mapping landslides from optical images. We adapted these implementations by replacing the optical ...
For example, Hunt (1974) identified two general problem-solving algorithms that could be used to solve the matrices. One algorithm is an analytic strategy that “applies logical operations to features contained within elements of the problem matrix” (Hunt, 1974, p. 133), while the other is ...
Cluster analysis is a primary method for database mining. It is either used as a stand-alone tool to get insight into the distribution of a data set, e.g. to focus further analysis and data processing, or as a preprocessing step for other algorithms operating on the detected clusters. Alm...
Deep learning-based inpainting algorithms for these datasets are broadly classified into two categories: convolutional neural networks (CNNs) and generative adversarial networks (GANs). 2.1 Image inpainting datasets The Places2 dataset is widely used in image inpainting research [27]. It is designed ...
We present a method that can be seen as an improvement of standard progressive sampling method. The method exploits information concerning performance of a given algorithm on past datasets, which is used to generate predictions of the stopping point. Experimental evaluation shows that the method can...
Our experiments demonstrate that ProFeat outperforms previous state-of-the-art algorithms in terms of clustering accuracy, and that our ensemble-based approach further improves clustering accuracy. 2. Related work Clustering has been extensively studied over decades in the machine learning and computer ...