Overall accuracies (%), kappa coefficients and classification speed using various classifiers based on SPOT image.

<p>Overall accuracies (%), kappa coefficients and classification speed using various classifiers based on SPOT image.</p

Classification maps for the test area in northern China using various classifiers under the same training case (90 training samples for each class, 10 features).

<p>(a) MLC algorithm. (b) BPN algorithm,  = 0.5,  = 0.8,  = 10–20,  = 10–6. (c) PSVM algorithm, c = 213,  = 2–13. (d) FNPSVM, t1 = 0.1, t2 = 0.8, c = 213 and  = 2–13.</p

Means and standard deviations () of overall classification accuracies based on various samples and features using ETM⁺ image.

<p>Means and standard deviations () of overall classification accuracies based on various samples and features using ETM⁺ image.</p

The overall accuracies (%) and kappa coefficients of the second layer grid-search using 5-time random-validation based on ETM⁺ image.

<p>The overall accuracies (%) and kappa coefficients of the second layer grid-search using 5-time random-validation based on ETM⁺ image.</p

Original SPOT image in study area (composite of bands 3, 2 and 1).

<p>Original SPOT image in study area (composite of bands 3, 2 and 1).</p

Classification maps for the western part of test area of Da’an city in China using various classifiers under the same training cases (120 training samples for each class, 8 features) based on SPOT image.

<p>(a) MLC algorithm. (b) BPN algorithm,  = 0.3,  = 0.8,  = 10⁻²⁰,  = 10⁻⁶. (c) PSVM algorithm, c = 2¹¹,  = 2⁻¹³. (d) FNPSVM, t1 = 0.1, t2 = 0.8, c = 2¹¹ and  = 2⁻¹³.</p

The overall accuracies (%) and kappa coefficients of the second layer grid-search using 6-time random-validation based on SPOT image.

<p>The overall accuracies (%) and kappa coefficients of the second layer grid-search using 6-time random-validation based on SPOT image.</p

Training time and classification time of whole data set (4,037,099 pixels) using various classifiers on different cases based on ETM⁺ image unit:second.

<p>Training time and classification time of whole data set (4,037,099 pixels) using various classifiers on different cases based on ETM⁺ image unit:second.</p

Univariate and multivariate analyses of risk factors for STH infection (dependent variable) for sampled children in Guizhou and Sichuan, 2010.

<p>NOTE. ^aCategorical data are no. (%) of subjects, continuous data are expressed as mean (SD)</p><p>OR = odds ratio; CI = confidence interval.</p

Figure of Fuzzy Membership Function: <i>t</i>₁ and <i>t</i>₂ that tune the fuzzy membership of each data point in the training are two user-defined constants, and they determine the range in which the data sample absolutely does or does not belong to a given class.

<p>Figure of Fuzzy Membership Function: <i>t</i>₁ and <i>t</i>₂ that tune the fuzzy membership of each data point in the training are two user-defined constants, and they determine the range in which the data sample absolutely does or does not belong to a given class.</p