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- W2313122685 abstract "Recent years have witnessed a dramatic increase in our ability to collect data from various sensors, devices, in different formats, from independent or connected applications. This data flood has outpaced our capability to process, analyze, store and understand these datasets. Many machine learning algorithms do not scale beyond data sets of a few million elements or cannot tolerate the statistical noise and gaps found in real-world data. Further research is required to develop algorithms that apply in real-world situations and on data sets of trillions of elements. In this paper we propose a scalable algorithm to handle the huge collections of data. Through the experimental results, proposed method performs well on huge data from UCI machine learning repository data Set." @default.
- W2313122685 created "2016-06-24" @default.
- W2313122685 creator A5043161568 @default.
- W2313122685 date "2015-06-28" @default.
- W2313122685 modified "2023-09-23" @default.
- W2313122685 title "Incremental Eigenspace Model Applied to Real World Problem" @default.
- W2313122685 cites W1596717185 @default.
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- W2313122685 doi "https://doi.org/10.14257/astl.2015.98.09" @default.
- W2313122685 hasPublicationYear "2015" @default.
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