15:[["$","meta","0",{"name":"viewport","content":"width=device-width, initial-scale=1"}],["$","meta","1",{"charSet":"utf-8"}],["$","title","2",{"children":"dots() R function from [kerndwd] | R PACKAGES"}],["$","meta","3",{"name":"description","content":"Kernel functions provided in the R package `kernlab`. Details can be seen in the reference below.\n\nThe Gaussian RBF kernel $k(x, x') = exp(-\\sigma|x - x'|^2)$\n\nThe Polynomial kernel $k(x,x') = (scale + offset)^degree$\n\nThe Linear kernel $k(x, x') = $\n\nThe Laplacian kernel $k(x, x') = exp(-\\sigma|x - x'|)$\n\nThe Bessel kernel $k(x, x') = (-Bessel{(\\nu+1)}^n \\sigma|x - x'|^2)$\n\nThe ANOVA RBF kernel c(\"$k(x, x') = sum_{1 <= i_1, ..., < i_D <= N} \\n$\", \"$ prod_{d=1}^D k(x_id, x'_id)$\") where k(x, x) is a Gaussian RBF kernel.\n\nThe Spline kernel c(\"$prod_{d=1}^D 1 + x_i x_j + x_i x_j min(x_i,\\n$\", \"$ x_j) - (x_i + x_j)/2 min(x_i,x_j)^2 +\\n$\", \"$ (min(x_i,x_j)^3)/3$\"). The parameter `sigma` used in `rbfdot` can be selected by `sigest()`."}],["$","meta","4",{"property":"og:title","content":"dots() R function from [kerndwd]"}],["$","meta","5",{"property":"og:description","content":"Kernel functions provided in the R package `kernlab`. Details can be seen in the reference below.\n\nThe Gaussian RBF kernel $k(x, x') = exp(-\\sigma|x - x'|^2)$\n\nThe Polynomial kernel $k(x,x') = (scale + offset)^degree$\n\nThe Linear kernel $k(x, x') = $\n\nThe Laplacian kernel $k(x, x') = exp(-\\sigma|x - x'|)$\n\nThe Bessel kernel $k(x, x') = (-Bessel{(\\nu+1)}^n \\sigma|x - x'|^2)$\n\nThe ANOVA RBF kernel c(\"$k(x, x') = sum_{1 <= i_1, ..., < i_D <= N} \\n$\", \"$ prod_{d=1}^D k(x_id, x'_id)$\") where k(x, x) is a Gaussian RBF kernel.\n\nThe Spline kernel c(\"$prod_{d=1}^D 1 + x_i x_j + x_i x_j min(x_i,\\n$\", \"$ x_j) - (x_i + x_j)/2 min(x_i,x_j)^2 +\\n$\", \"$ (min(x_i,x_j)^3)/3$\"). The parameter `sigma` used in `rbfdot` can be selected by `sigest()`."}],["$","meta","6",{"property":"og:url","content":"https://r-packages.io/packages/kerndwd/dots"}],["$","meta","7",{"property":"og:site_name","content":"R PACKAGES"}],["$","meta","8",{"property":"og:locale","content":"en_US"}],["$","meta","9",{"property":"og:image:type","content":"image/png"}],["$","meta","10",{"property":"og:image","content":"https://r-packages.io/packages/kerndwd/dots/opengraph-image?6cdc95b0c38aa621"}],["$","meta","11",{"property":"og:image:width","content":"1200"}],["$","meta","12",{"property":"og:image:height","content":"630"}],["$","meta","13",{"property":"og:type","content":"website"}],["$","meta","14",{"name":"twitter:card","content":"summary_large_image"}],["$","meta","15",{"name":"twitter:title","content":"dots() R function from [kerndwd]"}],["$","meta","16",{"name":"twitter:description","content":"Kernel functions provided in the R package `kernlab`. Details can be seen in the reference below.\n\nThe Gaussian RBF kernel $k(x, x') = exp(-\\sigma|x - x'|^2)$\n\nThe Polynomial kernel $k(x,x') = (scale + offset)^degree$\n\nThe Linear kernel $k(x, x') = $\n\nThe Laplacian kernel $k(x, x') = exp(-\\sigma|x - x'|)$\n\nThe Bessel kernel $k(x, x') = (-Bessel{(\\nu+1)}^n \\sigma|x - x'|^2)$\n\nThe ANOVA RBF kernel c(\"$k(x, x') = sum_{1 <= i_1, ..., < i_D <= N} \\n$\", \"$ prod_{d=1}^D k(x_id, x'_id)$\") where k(x, x) is a Gaussian RBF kernel.\n\nThe Spline kernel c(\"$prod_{d=1}^D 1 + x_i x_j + x_i x_j min(x_i,\\n$\", \"$ x_j) - (x_i + x_j)/2 min(x_i,x_j)^2 +\\n$\", \"$ (min(x_i,x_j)^3)/3$\"). The parameter `sigma` used in `rbfdot` can be selected by `sigest()`."}],["$","meta","17",{"name":"twitter:image:type","content":"image/png"}],["$","meta","18",{"name":"twitter:image","content":"https://r-packages.io/packages/kerndwd/dots/opengraph-image?6cdc95b0c38aa621"}],["$","meta","19",{"name":"twitter:image:width","content":"1200"}],["$","meta","20",{"name":"twitter:image:height","content":"630"}],["$","link","21",{"rel":"icon","href":"/favicon.ico","type":"image/x-icon","sizes":"16x16"}],["$","meta","22",{"name":"next-size-adjust"}]]

dots function