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Diffstat (limited to 'docs/mllib-optimization.md')
| -rw-r--r-- | docs/mllib-optimization.md | 8 |
1 files changed, 4 insertions, 4 deletions
diff --git a/docs/mllib-optimization.md b/docs/mllib-optimization.md index 396b98d52a..c79cc3d944 100644 --- a/docs/mllib-optimization.md +++ b/docs/mllib-optimization.md @@ -95,12 +95,12 @@ As an alternative to just use the subgradient `$R'(\wv)$` of the regularizer in direction, an improved update for some cases can be obtained by using the proximal operator instead. For the L1-regularizer, the proximal operator is given by soft thresholding, as implemented in -[L1Updater](api/mllib/index.html#org.apache.spark.mllib.optimization.L1Updater). +[L1Updater](api/scala/index.html#org.apache.spark.mllib.optimization.L1Updater). ## Update Schemes for Distributed SGD The SGD implementation in -[GradientDescent](api/mllib/index.html#org.apache.spark.mllib.optimization.GradientDescent) uses +[GradientDescent](api/scala/index.html#org.apache.spark.mllib.optimization.GradientDescent) uses a simple (distributed) sampling of the data examples. We recall that the loss part of the optimization problem `$\eqref{eq:regPrimal}$` is `$\frac1n \sum_{i=1}^n L(\wv;\x_i,y_i)$`, and therefore `$\frac1n \sum_{i=1}^n L'_{\wv,i}$` would @@ -138,7 +138,7 @@ are developed, see the section for example. The SGD method -[GradientDescent.runMiniBatchSGD](api/mllib/index.html#org.apache.spark.mllib.optimization.GradientDescent) +[GradientDescent.runMiniBatchSGD](api/scala/index.html#org.apache.spark.mllib.optimization.GradientDescent) has the following parameters: * `gradient` is a class that computes the stochastic gradient of the function @@ -161,6 +161,6 @@ each iteration, to compute the gradient direction. Available algorithms for gradient descent: -* [GradientDescent.runMiniBatchSGD](api/mllib/index.html#org.apache.spark.mllib.optimization.GradientDescent) +* [GradientDescent.runMiniBatchSGD](api/scala/index.html#org.apache.spark.mllib.optimization.GradientDescent) |