My wish is to describe the time complexity of several classification approaches. For example, suppose we have $n$ data points in $m$ dimensional space and a binary class variable. We do not assume anything about its distribution (it may be symmetric or very skewed). In statistics, we choose a predictive model and fit its parameters. What is the time complexity of fittingnaive Bayesian classifierlogistic regression$k$-nearest neighbor classifierSVM $\ldots$I understand that in some cases additional parameters or assumptions about the data (types of variables) are required. These parameters can be of course incorporated in your answer. Thank you very much!
Computational complexity of learning (classification) algorithms - fitting the parameters
lg.learning;machine learning