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A multioutput support vector machine (MSVM) is used to map the complex, nonlinear relationship between the in situ stress, elastic parameters, and borehole pressure.
The relationship between the borehole pressure and geomechanical parameters can be derived by the MSVM. The basic idea of MSVM is to extend the single-output support vector machine to a multidimensional output case.
A brief description and the MSVM algorithm can be found in the literature .
Based on the above MSVM model, the nonlinear relationship between the borehole pressure and geomechanical parameters can be described as
In SAE 10-7 row, MSVM increased 11% in terms of classification accuracy.
MSVM, LDA, and NB increased classification accuracy but still lack stability.
Classifier Classification Max Mean Min accuracy (%) KELM ACC 83.23 71.32 68.49 LSVM ACC 81.05 64.21 44.21 MSVM ACC 84.21 61.98 43.16 RSVM ACC 85.26 74.34 69.47 CART ACC 89.47 73.95 58.95 KNN ACC 90.53 82.76 76.84 LDA ACC 87.37 69.61 53.68 NB ACC 75.79 69.74 61.05 TABLE 4: Results of comparative classifiers with SAE on Oxford Dataset.
TUESDAY, June 19, 2018 (HealthDay News) -- A substantial proportion of women older than 60 years may experience moderate-to-severe vasomotor symptoms (msVMS), according to a study published online May 7 in Menopause.
David, M.D., from the Mayo Clinic in Scottsdale, Ariz., and colleagues surveyed 4,956 women presenting for menopause consultation to determine frequency of msVMS in women 60 years of age or older and examined their characteristics to determine factors that may be associated with VMS in older women.
"A substantial number of women seen in a specialty menopause clinic were over age 60 years and reported msVMS, highlighting that VMS may be disruptive in women over a decade past the natural age of menopause," the authors write.
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- MSW 1
- MSW 1.0
- MSW 2
- MSW 2.0
- MSW 2000
- MSW 2k
- MSW 3
- MSW 3.0
- MSW 3.1
- MSW 3.11
- MSW 95