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Regularized discriminant for supervised problems with noisy data
Gradient Boosted Trees.
Gaussian Process classifier - it gives probabilistic predictions (which is useful when your operational relative class frequencies differ from those in your training set, or equivalenty your false-positive/false-negative costs are unknown or variable). It also provides an inidcation of the uncertainty in model predictions due to the uncertainty in "estimating the model" from a finite dataset. The co-variance function is equivalent to the kernel function in an SVM, so it can also operate directly on non-vectorial data (e.g. strings or graphs etc). The mathematical framework is also neat (but don't use the Laplace approximation). Automated model selection via maximising marginal likelihood.
Essentially combines good features of logistic regression and SVM.
L1-regularized logistic regression.
K-means clustering for unsupervised learning.