What are some general pros and cons of various classification and regression techniques?

I realize this question could be difficult to answer, as the performance of certain methods will vary from case to case, but I'm looking to put together a list of some general pros and cons of methods like SVMs, ANNs, GLMs, etc.. These might be well documented characteristics of the method (in which case I would love some references), or I'm even interested in hearing your experience. Certain characteristics I'm looking for are

• Accuracy/Performance
• Difficulty to train / Training times for large data sets
• Tendecy to over fit
• Ablility to handel noisy data
• Requires assumptions
  

A good example of what I'm looking for is on the wiki page for Random Forests. I wish more machine learning pages had a list like this.

I'm hoping a discussion of this will help with model selection. Thanks /r/MachineLearning!