Results 281 to 290 of about 510,572 (297)
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2018
FL is a technique that models the behavior of data from a subset of attributes only. It also shows the correlation between detection performance and traffic model quality efficiently (Palmieri et al., Concurrency Comput Pract Exp 26(5):1113–1129, 2014). However, feature extraction and feature selection are different.
Kwangjo Kim +2 more
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FL is a technique that models the behavior of data from a subset of attributes only. It also shows the correlation between detection performance and traffic model quality efficiently (Palmieri et al., Concurrency Comput Pract Exp 26(5):1113–1129, 2014). However, feature extraction and feature selection are different.
Kwangjo Kim +2 more
openaire +1 more source
Neural Bag-of-Features learning
Pattern Recognition, 2017zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Passalis, Nikolaos, Tefas, Anastasios
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Feature Construction during Tree Learning
1991This paper addresses the “problem of new terms” in the context of learning decision trees using the approach based on ID3. We discuss an algorithm for efficiently constructing new features from given primitive features and relate it to constructive induction.
Gerhard Mehlsam +2 more
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Adaptation? Learning? Features of biological learning
2012 International Symposium on Micro-NanoMechatronics and Human Science (MHS), 2012All living organisms can learn the behavior appropriate to the environment. The learning process is, however, seems to be the convergence to the adapted behavior rather than learning. Living organisms can create the appropriate behavior through the behavior control in the environment.
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IEEE Transactions on Neural Networks and Learning Systems
Unsupervised feature selection is challenging in machine learning, pattern recognition, and data mining. The crucial difficulty is to learn a moderate subspace that preserves the intrinsic structure and to find uncorrelated or independent features simultaneously.
Pei Huang, Mengying Xie, Xiaowei Yang
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Unsupervised feature selection is challenging in machine learning, pattern recognition, and data mining. The crucial difficulty is to learn a moderate subspace that preserves the intrinsic structure and to find uncorrelated or independent features simultaneously.
Pei Huang, Mengying Xie, Xiaowei Yang
openaire +2 more sources