Pattern Recognition and Machine Learning
Citations
134 citations
Cites background or methods from "Pattern Recognition and Machine Lea..."
...Bishop (2006) has illustrated that transformed variables using a logistic sigmoid function gains more optimal decision boundary for classification compared to non-transformed variables....
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...To assign weights to classes of spatial evidence in order to create predictor maps, either knowledgeor data-driven methods for MPM are used (Bonham-Carter 1994; Carranza 2008). With knowledge-driven methods, which are appropriate in greenfield (or poorly explored) areas, subjective judgment of an expert analyst is employed in assigning weights to classes of a spatial evidence layer. The theory of fuzzy sets and fuzzy logic (Zadeh 1965) has been successfully applied in knowledge-driven MPM. In fuzzy logic MPM, the fuzzy weights assigned to spatial evidence must reflect realistic spatial associations between spatial evidence and mineral deposits of the type sought. Fuzzification, or assignment of fuzzy weights, is the most important stage in fuzzy logic MPM (Carranza 2008). Recent examples of fuzzy logic MPM are found in D’Ercole et al. (2000); Knox-Robinson (2000); Porwal & Sides (2000); Venkataraman et al. (2000); Carranza & Hale (2001); Porwal et al. (2003, 2004, 2006), Tangestani & Moore (2003); Ranjbar & Honarmand (2004); Eddy et al....
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...To assign weights to classes of spatial evidence in order to create predictor maps, either knowledgeor data-driven methods for MPM are used (Bonham-Carter 1994; Carranza 2008). With knowledge-driven methods, which are appropriate in greenfield (or poorly explored) areas, subjective judgment of an expert analyst is employed in assigning weights to classes of a spatial evidence layer. The theory of fuzzy sets and fuzzy logic (Zadeh 1965) has been successfully applied in knowledge-driven MPM. In fuzzy logic MPM, the fuzzy weights assigned to spatial evidence must reflect realistic spatial associations between spatial evidence and mineral deposits of the type sought. Fuzzification, or assignment of fuzzy weights, is the most important stage in fuzzy logic MPM (Carranza 2008). Recent examples of fuzzy logic MPM are found in D’Ercole et al. (2000); Knox-Robinson (2000); Porwal & Sides (2000); Venkataraman et al. (2000); Carranza & Hale (2001); Porwal et al. (2003, 2004, 2006), Tangestani & Moore (2003); Ranjbar & Honarmand (2004); Eddy et al. (2006); Rogge et al....
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...Following Bishop (2006), we have provided here Figure 5 for further illustration of the better performance of logistic transformation (non-linear) compared to linear transformation to discriminate between background and anomaly....
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...Non-linear transformation gains an optimal decision boundary between different classes of a variable for classification purposes (Bishop 2006)....
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134 citations
134 citations
133 citations
Cites background from "Pattern Recognition and Machine Lea..."
...This is arguably the most natural distribution on Rn, especially from a machine learning perspective [8, 23, 32, 37]....
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133 citations
Additional excerpts
...The generated feature set, which is a nonlinear combination of the initial features, was put into a machine learning classifier [58] to predict buggy commits....
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