A traditional discriminative approach, not premised on any generative model, involves the computation of hyperplanes that partition the clusters of known class instances from one another (optimizing certain metrics involving in-class and between-class variance); new instances are assigned to the class into whose partition they fall. Perceptrons provide a related technique, insofar as they decide class membership on the basis of a linear combination of feature values; their particular advantage is that they can learn incrementally (by adjusting feature weights) as more and more training data become available. Another durable discriminative approach—not dependent on linear separability of classes—is the k nearest neighbors (kNN) method, which assigns an unknown text or document to the class that is most prevalent among its k (e.g., 1–5) nearest neighbors in vector space. While all the previously mentioned methods depended on parameter estimation (e.g., generative probabilities, Gaussian parameters, or coefficients of separating planes), kNN uses no such parameters—it is nonparametric; however, finding a suitable measure of proximity or similarity can be challenging, and errors due to haphazard local data point configurations in feature space are hard to avoid. Another nonparametric discriminative method worth mentioning is the use of decision trees, which can be learned using information-theoretic techniques; they enable choice of a class by following a root-to-leaf path, with branches chosen via tests on features of a given input vector. A potentially useful property is that learned decision trees can provide insight into what the most important features are (such insight can also be provided by dimensionality reduction methods). However, decision trees tend to converge to nonglobal optima (global optimization is NP-hard), and by splitting data, tend to block modeling of feature interactions; this defect can be alleviated to some extent through the use of decision forests. 