Neural Incremental Attribute Learning in Groups
نویسندگان
چکیده
منابع مشابه
Neural Incremental Attribute Learning in Groups
Incremental Attribute Learning (IAL) is a feasible approach for solving high-dimensional pattern recognition problems. It gradually trains features one by one. Previous research indicated that supervised machine learning with input attribute ordering can improve classification results. Moreover, input space partitioning can also effectively reduce the interference among features. This study pro...
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Machine learning is a very important approach to pattern classification. This paper provides a better insight into Incremental Attribute Learning (IAL) with further analysis as to why it can exhibit better performance than conventional batch training. IAL is a novel supervised machine learning strategy, which gradually trains features in one or more chunks. Previous research showed that IAL can...
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Feature ordering is a significant data preprocessing method in Incremental Attribute Learning (IAL), a novel machine learning approach which gradually trains features according to a given order. Previous research has shown that, similar to feature selection, feature ordering is also important based on each feature’s discrimination ability, and should be sorted in a descending order of their dis...
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Adaptation plays a central role in dynamically changing systems. It is about the ability of the system to “responsively” self-adjust upon change in the surrounding environment. Like in living creatures that have evolved over millions of years developing ecological systems due to their self-adaptation and fitness capacity to the dynamic environment, systems undergo similar cycle to improve or at...
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ژورنال
عنوان ژورنال: International Journal of Computational Intelligence Systems
سال: 2015
ISSN: 1875-6891,1875-6883
DOI: 10.1080/18756891.2015.1023587