Currently the most hyped new machine learning technique. It has a good generalisation performance and is usable for many practical problems. Support Vector Machines can be applied to many problems, including classification, regression, novelty detection and page ranking. Initially only made for creating models for linearly seperable data, extensions to (almost) arbitrary models have been made by using the kernel trick. This requires prior knowledge or cross-validation to find the best kernel. SVM scale somewhat well with the number of examples and dimensions, but have difficulties dealing with very high dimensional problems or 100k's of examples. However, there are very impressive results from SVM's out there.
A very promissing new approach for classification and regression problems.
A promissing new approach for regression problems. The really cool thing about this one is that it can cope really well with very high dimensional problems.