Natural Neighbor Reduction Algorithm for Instance-based Learning
dl.acm.org › abs › IJCINI.2016100103
Oct 1, 2016 · Instance reduction is aimed at reducing prohibitive computational costs and the storage space for instance-based learning.
Downloadable! Instance reduction is aimed at reducing prohibitive computational costs and the storage space for instance-based learning.
In this paper, a new hybrid algorithm called instance reduction algorithm based on natural neighbor and nearest enemy is presented. At first, an edition ...
In this study, we present a natural neighborhood graph-based instance reduction algorithm, namely, NNGIR. A natural neighborhood graph (NaNG) is automatically ...
For example, the basic nearest neighbor algorithm retains all of the training instances. It learns very quickly because it need only read in the training set ...
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What is the algorithm for instance-based learning?
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Examples of instance-based learning algorithms are the k-nearest neighbors algorithm, kernel machines and RBF networks. ... "Reduction techniques for instance- ...
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This work discusses the concept of data seriation and its application on instance-based learning, and introduces a new approach, the Instance Seriation for ...
This paper has two main purposes. First, it provides a survey of existing algorithms used to reduce storage requirements in instance-based learning algorithms.
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摘要. Instance reduction is aimed at reducing prohibitive computational costs and the storage space for instance-based lear.
In this study, we present a natural neighborhood graph-based instance reduction algorithm, namely, NNGIR. A natural neighborhood graph (NaNG) is automatically ...