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Learning a Unified Classifier Incrementally via Rebalancing
Computer Vision and Pattern Recognition, 2019Conventionally, deep neural networks are trained offline, relying on a large dataset prepared in advance. This paradigm is often challenged in real-world applications, e.g. online services that involve continuous streams of incoming data.
Saihui Hou +4 more
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Classify wealth to classify societies
La Pensée, 2023Identifier les deux formes de la richesse permet d’opérer une dichotomie au sein des sociétés humaines. Dans le premier groupe, les droits de propriété sur les biens ne peuvent être convertis qu’en droits de propriété sur d’autres biens : la richesse, en quelque sorte refermée sur elle-même, reste alors d’une importance marginale. Dans le second groupe,
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TO CLASSIFY OR NOT TO CLASSIFY. . .
The Serials Librarian, 1978Serials have been treated variously in libraries with regard to classification. The general feeling is that classification is a waste of time and a problem for the patron. The author disagrees and advocates full classification of serials because: (a) materials on the same subject are kept together, expanding subject access and facilitating browsing; (b)
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A Meta Classifier by Clustering of Classifiers
2014To learn any problem, many classifiers have been introduced so far. Each of these classifiers has many strengths (positive aspects) and weaknesses (negative aspects) that make it suitable for some specific problems. But there is no powerful solution to indicate which classifier is the best classifier (or at least a good one) for a special problem ...
Mohammad Iman Jamnejad +3 more
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Hybridizing Ensemble Classifiers with Individual Classifiers
2009 Ninth International Conference on Intelligent Systems Design and Applications, 2009Two extensive research areas in Machine Learning are classification and prediction. Many approaches have been focused in the induction of ensemble to increase learning accuracy of individual classifiers. Recently, new approaches, different to those that look for accurate and diverse base classifiers, are emerging. In this paper we present a system made
Gonzalo Ramos-Jiménez +2 more
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Neural Computation, 1998
We present a new classification architecture based on autoassociative neural networks that are used to learn discriminant models of each class. The proposed architecture has several interesting properties with respect to other model-based classifiers like nearest-neighbors or radial basis functions: it has a low computational complexity and uses a ...
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We present a new classification architecture based on autoassociative neural networks that are used to learn discriminant models of each class. The proposed architecture has several interesting properties with respect to other model-based classifiers like nearest-neighbors or radial basis functions: it has a low computational complexity and uses a ...
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Decision tree classifier: a detailed survey
International Journal of Information and Decision Sciences, 2020Decision tree classifier (DTC) is one of the well-known methods for data classification. The most significant feature of DTC is its ability to change the complicated decision making problems into simple processes, thus finding a solution which is ...
Priyanka, Dharmender Kumar
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TO CLASSIFY OR NOT TO CLASSIFY ... A REJOINDER
The Serials Librarian, 1981Classification of journals is not always advisable. Reasons are given for filing science journals by title, and suggestions are made concerning the arrangement of science collections.
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To classify or not to classify, that is the question?
Legal Information Management, 2002I recently found myself in the ‘enviable’ position of purchasing a library management system. The whole process gave me the chance to review the current classification scheme. The classification used was a ‘home made’ scheme based on broad legal topics.
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Coeliac disease: to classify or not to classify - that is the question!
Gastroenterology and hepatology from bed to bench, 2016.
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