Computational complexity of learning (classification) algorithms - fitting the parameters
"Training" a naive bayes classifier, assuming you're given the feature vectors, is $O(n)$, where $n = \sum_i nnz(x_i)$, where $nnz(x_i)$ is the number of nonzero features in data point $i$.

For logistic regression you have to solve an optimization problem, and this depends on the optimizer. With L-BFGS an iteration takes linear time on the size of the training set and I've never needed more than 100 iterations to converge to a reasonable value (but I'm not aware of any worst-case bounds).

For the $k$-nearest neighbor classifier there is no training, but classifying has a high cost, and depends on the search algorithm you're using. For linear search the complexity is (for fixed k) proportional to the size of the training set for each point you want to classify. With approximate algorithms this can be brought down considerably at the expense of accuracy.

For svms standard quadratic-programming solvers are $n^3$, but recent approaches are inverse in the size of the training set. More information in this quora answer.

If you really need fast learning the best approaches so far are those based on online learning, which can converge to classifiers that compete well against batch classifiers with only one pass over the training data. A very fast implementation is vowpal wabbit and a good algorithm is confidence-weighted learning.