Combining MLC and SVM classifiers for learning based decision making: analysis and evaluations
Citations
137 citations
Cites methods from "Combining MLC and SVM classifiers f..."
...(1) SVM: SVM is the conventional shallow structured classifier [Zhang et al. 2015a] and is set as the baseline for comparisons....
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...We compare the proposed generalized DTNs (sig-tDTNs and duft-tDTNs) to the following methods: (1) SVM: SVM is the conventional shallow structured classifier [Zhang et al. 2015a] and is set as the baseline for comparisons....
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130 citations
Cites methods from "Combining MLC and SVM classifiers f..."
...For regression purpose, a linear SVM is adopted for its simplicity and effectiveness[23][102]....
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128 citations
Cites methods from "Combining MLC and SVM classifiers f..."
...There were many popular algorithms concerning about Classifier Combination; such as Bayesian [41], [42], Dempster–Shafer [43]–[47], Fuzzy Integral [48], [49], and Voting Methods [50]–[57]....
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23 citations
Additional excerpts
...[56] and Szuster et al....
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19 citations
Cites background or methods from "Combining MLC and SVM classifiers f..."
...…impact of urban impervious surfaces on environmental issues such as water and air pollution, flooding, and urban climate, the amount of impervious surfaces (IS) has been recognized as the most significant index of environmental quality (Arnold Jr and Gibbons 1996; Weng 2012; Zhang et al. 2015a)....
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...…it is also reported that the distribution of IS plays a crucial role in estimating numerous socioeconomic factors such as urban development, population distribution and density, social conditions, and fluctuation of housing prices (Wu and Murray 2003; Yuan and Bauer 2007; Zhang et al. 2015a)....
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...This algorithm is based on Bayesian theory in estimating parameters of a probabilistic model (Zhang et al. 2015b)....
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...Nevertheless, accuratemapping of impervious surfaces using satellite passive sensor data has been a challenging task due to the diversity of urban land cover classes, where confusion often occurs between pervious and impervious surfaces (Weng 2012; Zhang et al. 2015a, 2016; Ma et al. 2017b)....
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...A number of studies on the extraction of IS, including Slonecker et al. (2001), Bauer et al. (2005), Yuan and Bauer (2007), Weng (2012), Wang et al. (2015), Zhang et al. (2015a), and Wei and Blaschke (2018), have shown the effectiveness and reliability of remote sensing in the monitoring of UIS....
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References
50 citations
Additional excerpts
...Future work will focus on combining other classifiers such as neural network for applications in medical imaging [31-33] and recognition and classification tasks [34-35]....
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45 citations
Additional excerpts
...[27] MLC and SVM are found to be equivalent to each other in linear cases, and this can also be convinced by similar decision functions in (10) and (12)....
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42 citations
"Combining MLC and SVM classifiers f..." refers background in this paper
...Introduction Maximum likelihood classification (MLC) is one of the most commonly used approach in signal classification and identification, which has been successfully applied in a wide range of engineering applications including classification for digital amplitude-phase modulations [1], remote sensing [2], genes selection for tissue classification [3], nonnative speech recognition [4], chemical analysis in archaeological applications [5] and speaker recognition [6]....
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42 citations
"Combining MLC and SVM classifiers f..." refers background in this paper
...Future work will focus on combining other classifiers such as neural network for applications in medical imaging [31-33] and recognition and classification tasks [34-35]....
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33 citations
"Combining MLC and SVM classifiers f..." refers background in this paper
...Maximum likelihood classification (MLC) is one of the most commonly used approaches in signal classification and identification, which has been successfully applied in a wide range of engineering applications including classification for digital amplitude-phase modulations [1], remote sensing [2], genes selection for tissue classification [3], nonnative speech recognition [4], chemical analysis in archaeological applications [5], and speaker recognition [6]....
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