Having experimented with online-style learning in the previous chapter, you may have been surprised by its simplicity yet effectiveness and scalability in comparison to batch learning. In spite of learning just one example at a time, SGD can approximate the results well as if all the data resides in the core memory and you were using a batch algorithm. All you need is that your stream be indeed stochastic (there are no trends in data) and that the learner is tuned well to the problem (the learning rate is often the key parameter to be fixed).
Anyway, examining such achievements closely, the results are still just comparable to batch linear models but not to learners that are more sophisticated and characterized by higher variance than bias, such as SVMs, neural networks, or bagging and boosting ensembles of decision trees.
For certain problems, such as tall and wide but sparse data, just linear combinations may be enough according to the observation that...