According to the application features of wireless sensor networks, this paper proceeds a clustering model bases on event area, and uses EAECA (Event-Area Based Energy Optimum Clustering Algorithms) to select cluster heads, considers the active nodes in the event area as the clustering objects and...
This paper reviews the development and trend of data stream clustering and analyzes typical data stream clustering algorithms proposed in recent years, such as Birch algorithm, Local Search algorithm, Stream algorithm and CluStream algorithm. We also summarize the latest research achievements in this ...
The research actuality and new progress in clustering algorithm in recent years are summarized in this paper. First, the analysis and induction of some representative clustering algorithms have been made from several aspects, such as the ideas of algorithm, key technology, advantage and disadvantage....
In recent years, deep learning algorithms have further developed and matured. Deep learning learns a large amount of data and automatically extracts features, and then trains specific algorithm models to obtain output results3. Deep learning is widely used in various aspects, and target detection is...
Besides, the deep learning, which is part of a broader family of machine learning methods, can intelligently analyze the data on a large scale. In this paper, we present a comprehensive view on these machine learning algorithms that can be applied to enhance the intelligence and the ...
In this paper, we take the four-way shuttle system as the research object and establish the mathematical model of scheduling optimization based on the minimum time for the in/out operation optimization and path optimization scheduling problems of the fou
rcc_algorithms rce rcs_tnsa re_identification_risk readtwice realformer recs_ecosystem_creator_rl recursive_optimizer red-ace regnerf relc rembert remote_sensing_representations repnet representation_batch_rl representation_clustering representation_similarity reset_free_learning reso...
along with tools for rigorous statistical testing to compare different solutions. And for tasks where no supervision is available, unsupervised algorithms such as k-means clustering might be needed. Many of those algorithms can be implemented using histograms, but custom solutions often bring significant...
A great building requires a strong foundation. This book teaches basic Artificial Intelligence algorithms such as dimensionality, distance metrics, clustering, error calculation, hill climbing, Nelder Mead, and linear regression. These are not just foundational algorithms for the rest of the series, but...
*= Equal Contributors *Equal Contributors To deploy machine learning models on-device, practitioners use compression algorithms to shrink and speed up models while maintaining their high-quality output. A critical aspect of compression in practice is model comparison, including tracking many compression ...