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Cascading is a particular case of ensemble learning based on the concatenation of several classifiers, using all information collected from the output from a given classifier as additional information for the next classifier in the cascade.
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^ XML data bindings and SOAP serialization tools provide type-safe XML serialization of programming data structures into XML. Shown are XML values that can be placed in XML elements and attributes. ^ This syntax is not compatible with the Internet-Draft, but is used by some dialects of Lisp.
Several problem transformation methods exist. One of them is the Binary Relevance method (BR). Given a set of labels and a data set with instances of the form (,) where is a feature vector and is a set of labels assigned to the instance.
In machine learning, support vector machines (SVMs, also support vector networks [1]) are supervised max-margin models with associated learning algorithms that analyze data for classification and regression analysis.
Decision 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.
In SGML, HTML and XML documents, the logical constructs known as character data and attribute values consist of sequences of characters, in which each character can manifest directly (representing itself), or can be represented by a series of characters called a character reference, of which there are two types: a numeric character reference and a character entity reference.
An associative classifier (AC) is a kind of supervised learning model that uses association rules to assign a target value. The term associative classification was coined by Bing Liu et al., [1] in which the authors defined a model made of rules "whose right-hand side are restricted to the classification class attribute".