Decision tree learningDecision tree learning is a supervised learning approach used in statistics, data mining and machine learning. In this formalism, a classification or regression decision tree is used as a predictive model to draw conclusions about a set of observations. Tree models where the target variable can take a discrete set of values are called classification trees; in these tree structures, leaves represent class labels and branches represent conjunctions of features that lead to those class labels.
Sequential analysisIn statistics, sequential analysis or sequential hypothesis testing is statistical analysis where the sample size is not fixed in advance. Instead data are evaluated as they are collected, and further sampling is stopped in accordance with a pre-defined stopping rule as soon as significant results are observed. Thus a conclusion may sometimes be reached at a much earlier stage than would be possible with more classical hypothesis testing or estimation, at consequently lower financial and/or human cost.
Error (linguistics)In applied linguistics, an error is an unintended deviation from the immanent rules of a language variety made by a second language learner. Such errors result from the learner's lack of knowledge of the correct rules of the target language variety. A significant distinction is generally made between errors (systematic deviations) and mistakes (speech performance errors) which are not treated the same from a linguistic viewpoint. The study of learners' errors has been the main area of investigation by linguists in the history of second-language acquisition research.
Net (mathematics)In mathematics, more specifically in general topology and related branches, a net or Moore–Smith sequence is a generalization of the notion of a sequence. In essence, a sequence is a function whose domain is the natural numbers. The codomain of this function is usually some topological space. The motivation for generalizing the notion of a sequence is that, in the context of topology, sequences do not fully encode all information about functions between topological spaces.
Object-based languageThe term object-based language may be used in a technical sense to describe any programming language that uses the idea of encapsulating state and operations inside objects. Object-based languages need not support inheritance or subtyping, but those that do are also termed object-oriented. Object-based languages that do not support inheritance or subtyping are usually not considered to be true object-oriented languages.
Prototype-based programmingPrototype-based programming is a style of object-oriented programming in which behaviour reuse (known as inheritance) is performed via a process of reusing existing objects that serve as prototypes. This model can also be known as prototypal, prototype-oriented, classless, or instance-based programming. Prototype-based programming uses the process generalized objects, which can then be cloned and extended. Using fruit as an example, a "fruit" object would represent the properties and functionality of fruit in general.
