I'm not sure that classification necessarily makes a statement about the population(s) from which the data points are drawn. Classification, as you probably know, uses training data consisting of some "feature" vectors, each labelled with a specific class, to predict the class labels belonging to other unlabeled feature vectors. For example, we might use a patient's vital signs and a doctor's diagnosis to predict whether other patients are healthy or ill.
Some classifiers, called "generative classifiers", try to explicitly model the populations or data generating process that produces each class. For example, the Naive Bayes algorithm computes $P(\textrm{class}=c|\textrm{features})$ for each class $c$, assuming that the features are all independent. These models could reasonably be seen as statements about the population.
However, other classifiers look for differences between the classes without modeling the classes themselves; these are called discriminative classifiers. One classic example is the nearest neighbour classifier, which assigns an unlabeled example to the class of its closest neighbor (where close is defined in some sensible way for the problem). This doesn't seem like it contains much, if any, information about the populations from which the data points were drawn.
If you are interested in the difference between descriptive and inferential statistics, it might be more fruitful to think about the purpose of the analysis. A descriptive statistic, like the mean, might tell you how many trout are in a typical lake--they describe something. An inferential statistic, like a $t$-test, might tell you if there are typically more trout than bass in these lakes-- it lets you make a claim about a descriptive statistic.