منابع مشابه
Prototypes Based Relational Learning
Relational instance-based learning (RIBL) algorithms offer high prediction capabilities. However, they do not scale up well, specially in domains where there is a time bound for classification. Nearest prototype approaches can alleviate this problem, by summarizing the data set in a reduced set of prototypes. In this paper we present an algorithm to build Relational Nearest Prototype Classifier...
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We present an empirical analysis of symbolic prototype learners for synthetic and real domains. The prototypes are learned by modifying the minimum-distance classiier to solve problems with symbolic attributes, attribute weighting, and its inability to learn multiple prototypes for a class. These extensions are implemented in SNMC. In the second half of this paper, we provide empirical analysis...
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In this paper, we introduce new algorithms that perform clustering and feature weighting simultaneously and in an unsupervised manner. The proposed algorithms are computationally and implementationally simple, and learn a di1erent set of feature weights for each identi2ed cluster. The cluster dependent feature weights o1er two advantages. First, they guide the clustering process to partition th...
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A writer independent handwriting recognition system must be able to recognize a wide variety of handwriting styles, while attempting to obtain a high degree of accuracy when recognizing data from any one of those styles. As the number of writing styles increases, so does the variability of the data’s distribution. We then have an optimization problem: how to best model the data, while keeping t...
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ژورنال
عنوان ژورنال: IEEE Annals of the History of Computing
سال: 2020
ISSN: 1058-6180,1934-1547
DOI: 10.1109/mahc.2020.2987408