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- W2155484134 abstract "Multi-attribute classification technology is currently widely used in seismic facies analysis, lithologic analysis, hydrocarbon detection, and reservoir parameter estimation. This technology can be divided into supervised and unsupervised methods depending on whether the training samples are used for classification. More attention has been paid to the AdaBoost method in recent years. It is a supervised classification method with a solid theoretical foundation, and has yielded good results in practice. The AdaBoost method is a general method for generating strong classifiers out of a set of weak classifiers. It brings about a new method and a new idea to the design of learning algorithm. In this paper, we first introduce the theory behind this method, and then apply it to a synthetic dataset. We show that it has higher accuracy compared to the k-nearest neighbour method. Finally, we apply the technology to two field data for gas detection and chimney channel identification, demonstrating that reliable results can be achieved." @default.
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- W2155484134 date "2014-01-01" @default.
- W2155484134 modified "2023-09-23" @default.
- W2155484134 title "AdaBoost-based Multi-attribute Classification Technology and Its Application" @default.
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- W2155484134 doi "https://doi.org/10.3997/2214-4609.20141200" @default.
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