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Pruning is a data compression technique in machine learning and search algorithms that reduces the size of decision trees by removing sections of the tree that are non-critical and redundant to classify instances. Pruning reduces the complexity of the final classifier, and hence improves predictive accuracy by the reduction of overfitting.
Noisy data are data with a large amount of additional meaningless information in it called noise. [1] This includes data corruption and the term is often used as a synonym for corrupt data. [1] It also includes any data that a user system cannot understand and interpret correctly. Many systems, for example, cannot use unstructured text. Noisy ...
Noisy text analytics is a process of information extraction whose goal is to automatically extract structured or semistructured information from noisy unstructured text data. While Text analytics is a growing and mature field that has great value because of the huge amounts of data being produced, processing of noisy text is gaining in ...
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Pruning [ edit ] The simplest methods, which can be particularly effective in conjunction with totally corrective training, are weight- or margin-trimming: when the coefficient, or the contribution to the total test error, of some weak classifier falls below a certain threshold, that classifier is dropped.
Noisy text is text with differences between the surface form of a coded representation of the text and the intended, correct, or original text. [1] The noise may be due to typographic errors or colloquialisms always present in natural language and usually lowers the data quality in a way that makes the text less accessible to automated processing by computers, including natural language ...
C5.0, which Quinlan is commercially selling (single-threaded version is distributed under the terms of the GNU General Public License), is an improvement on C4.5.The advantages are speed (several orders of magnitude faster), memory efficiency, smaller decision trees, boosting (more accuracy), ability to weight different attributes, and winnowing (reducing noise).
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