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Otherwise, we can simply use the algorithm of Hon et al. [10]. Thus, we present the first algorithm which needs o(n) words of additional space for computing SUS. We also present a randomized algorithm which reduces the above running time to O(nτlogc+1n) by using an additional n...
Time series classification has been the center of attention in the time series community for more than a decade. The problem typically refers to the task of inferring a model from a collection of labeled time series, which can be used to predict the class label of a new time series. Exampl...
However, concrete evidence that internet use (and in particular e-shopping) has impacted urban structure is still lacking (Ioannides et al., 2008), possibly because it takes years to substantially change the structure of an urban area, but also because of the complexity of the changes induced...
Overview of the reference models Furthermore, given the complexity of MTS data that are described in the previous section, few works have been introduced to solve forecasting applications using MTS datasets. Indeed, specific methodologies are required for treatment rather than traditional parametric metho...
79. This lack of agreement directly reflects the challenging and inherent subjective nature of the concept of toxicity. The complexity of the topic makes it particularly difficult to assess the reliability of natural language processing models for automatic toxicity detection despite the impressive ...
A single metric alert rule can monitor multiple resources, provided the resources are of the same type and exist in the same Azure region. Using this type of rule reduces complexity and the total number of alert rules you have to maintain. ...
Blueprinting.Develop a list of use cases across the value chain, prioritizing according to impact, complexity, and business value; then select the highest-need use cases. Digital and analytics solutions.Build and automate screening algorithms that link molecular descriptors (for example,...
However, the high time complexity of shapelet selection process hinders its application in real time data procession. To overcome this, in this paper we propose a fast shapelet selection algorithm (FSS), which sharply reduces the time consumption of shapelet selection. In our algorithm, we first ...
The prediction process of the random forest model was implemented using the Python programming platform. In the Python program, we used the “RandomizedSearchCV” function to approximate the random forest algorithm parameters. Then, the “GridSearchCV” function was used to accurately search the ...