Is there any difference between a weak learner and a weak classifier?
A weak learner can be either a classification or a regression algorithm: 


  Boosting (Schapire and Freund 2012) is a greedy algorithm for fitting adaptive basis-function models of the form in Equation 16.3, where the $\phi_m$ are generated by an algorithm called a weak learner or a base learner. The algorithm works by applying the weak learner sequentially to weighted versions of the data, where more weight is given to examples that were misclassified by earlier rounds.
  This weak learner can be any classification or regression algorithm, but it is common to use a CART model.


Source: "Machine Learning - A Probabilistic Perspective"; Murphy; 2012

So a weak classifier is simply a weak learner which is a classifier.