Results 271 to 280 of about 7,350 (305)

On an Improved SPRINT Data Stream Online Classification Algorithm

Advanced Materials Research, 2013
Aiming at the characteristics of data stream, the paper presents an incremental decision tree algorithm based on binary-attribute tree on the basis of SPRINT algorithm. The attribute set of this improved algorithm adopts the maximum entropy attribute classification and dynamic storage method of Bayesian method.
Hong Zhou, Chun Yan Fu, Jia Mei Xue, Yuan Zhi, Jing Shun Liu   +4 more
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Rival Learner Algorithm with Drift Adaptation for Online Data Stream Regression

Proceedings of the 2018 International Conference on Algorithms, Computing and Artificial Intelligence, 2018
Real-time extraction of meaningful data streams patterns is an increasingly important issue for machine learning and data mining communities. In this paper, we proposed a regression algorithm incremental on data streams which are infinite, high-speed and time-varying. The algorithm integrates two incremental model trees, global and local models.
Zhenwei Liao, Yongheng Wang
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A new online field feature selection algorithm based on streaming data

Journal of Ambient Intelligence and Humanized Computing, 2018
The rapid development of Internet technology derived out a massive network text data. Therefore, how to classify the massive text data efficiently has important theoretical significance and application value. In order to acquire accurate classification results, the process has been divided into two parts.
Zhenjiang Zhang, Fuxing Song, Peng Zhang, Han-Chieh Chao, Yingsi Zhao   +4 more
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A Power-Aware Online Scheduling Algorithm for Streaming Applications in Embedded MPSoC

2011
As application complexity grows, embedded systems move to multiprocessor architectures to cope with the computation needs. The issue formultiprocessor architectures is to optimize the processing resources usage and power consumption to reach a higher energy efficiency. These optimizations are handled by scheduling techniques.
Tanguy Sassolas, Nicolas Ventroux, Nassima Boudouani, Guillaume Blanc   +3 more
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Online stream clustering using density and affinity propagation algorithm

2013 IEEE 4th International Conference on Software Engineering and Service Science, 2013
In order to improve the data stream clustering accuracy and effectiveness, the paper proposes an efficient data stream clustering algorithm based on affinity propagation and density methods (APDenStream). The algorithm adopts online/offline two stage process framework, by using micro-cluster decay density to reflect the evolution information and using ...
null Jian-Peng Zhang, null Fu-Cai Chen, null Li-Xiong Liu, null Shao-Mei Li   +3 more
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An Online Anomalous Time Series Detection Algorithm for Univariate Data Streams

2013
We address the online anomalous time series detection problem among a set of series, combining three simple distance measures. This approach, akin to control charts, makes it easy to determine when a series begins to differ from other series. Empirical evidence shows that this novel online anomalous time series detection algorithm performs very well ...
Huaming Huang, Kishan Mehrotra, Chilukuri K. Mohan   +2 more
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Semi-supervised Learning Algorithm for Online Electricity Data Streams

2014
Recent developments in electricity market deregulation, the prices are not fixed. In such application, class labels are not available directly and potentially valuable information is lost. A learning model of electricity demand and prices needs to be adaptive for dynamic changes in massive data streams. This paper presents adaptive building of learning
Pramod Patil, Yogita Fatangare, Parag Kulkarni   +2 more
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Causality‐based online streaming feature selection

Concurrency Computation Practice and Experience, 2021
Longzhu Li, Yaojin Lin, Jinkun Chen
exaly  

An Unsupervised Online Streaming Feature Selection Algorithm with Density Peak Clustering

2023 IEEE International Conference on Networking, Sensing and Control (ICNSC), 2023
Yu Song, Zhigang Liu 0006
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Geometric Approximation Algorithms in the Online and Data Stream Models.

2008
The online and data stream models of computation have recently attracted considerable research attention due to many real-world applications in various areas such as data mining, machine learning, distributed computing, and robotics. In both these models, input items arrive one at a time, and the algorithms must decide based on the partial data ...
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