Add SVD-based solver to ridge regression.

I added a solver based on the thin SVD of X to compute the ridge
coefficients. This is much more stable than the cholesky decomposition
for singular matrices.

While in the Ridge regression the Gram matrix becomes nonsingular
because of the regularization, I've come across problems when the
regularization parameter is very small. This pull request remedies,
making the algorithm defined and stable even for the border case
alpha=0.

I took the liberty to change the default algorithm of to this one
(when there are no sample weights).
Remove unnecessary code in ridge svd
BUG: solver was not passed to computational method in Ridge object
Use Cholesky solver by default, but use SVD as fallback

when the cholesky fails.
Use ValueError for non-existant solvers
DOC rename lambda to alpha in plot_lasso_coordinate_descent_path. (Re)-Closes #903.
DOC clarification of parameter search
AdaBoost: use estimator weights in predict_proba
TST + DOC AdaBoostClassifier.predict_proba fix
COSMIT refactor AdaBoost code
DOC fetch_20newsgroups returns the text, not text files. see SO question: http://stackoverflow.com/questions/16615523/using-scikits-kmeans-to-cluster-ones-own-documents
DOC Fixed documentation of kernel parameters: sigm uses gamma, but not degree. Closes #1972.
ENH add multilabel support to PRF metric family

Support also in classification_report
DOC fix typos
COSMIT shorten long line for pep8
ignore PDFs
ENH Create FeatureSelectionMixin for shared [inverse_]transform code

Also rename FeatureSelectionMixin -> SelectorMixin -> _LearntSelectorMixin
And rename sklearn.feature_selection.{selector_mixin -> from_model}
DOC rewrite descriptions of P/R/F averages and define support
DOC/COSMIT fix typos in What's New
DOC add some contributions to What's New