Like most other techniques for training linear classifiers, the perceptron generalizes naturally to multiclass classification. In machine learning, the perceptron is an algorithm for supervised learning of binary classifiers. Algorithms with this basic setup are known as linear classifiers. Support vector machines and simpler methods such as linear classifiers gradually overtook neural networks. Many applications exist that are efficiently implemented using hierarchical classifiers or variants thereof. Additive smoothing is commonly a component of naive Bayes classifiers. More information on training and testing classifiers is at statistical classification. This can be generalised to weighted nearest neighbour classifiers. This section discusses strategies of extending the existing binary classifiers to solve multi-class classification problems. These compute classifiers by different approaches, differing in the degree of statistical modelling. For example, random forests perform better than SVM classifiers for 3D point clouds. There are at least five sets of numerical classifiers in Pingelapese. Matching-score level fusion consolidates the scores generated by multiple classifiers pertaining to different modalities. An analogous result on the strong consistency of weighted nearest neighbour classifiers also holds. Finite-state machines can be subdivided into acceptors, classifiers, transducers and sequencers.