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This paper compares the enhancement in information when filtering these noisy EEG signals while using a Schrodinger wave equation (SWE) based Recurrent Quantum Neural Network (RQNN) model and a Savitzky Golay (SG) filtering model, while investigating over multiple classification techniques on several datasets.
Multiple learners (base learners) are trained to solve the same problem by averaging over multiple classification models with different input feature vectors.
These ensemble techniques have the advantage to alleviate the small sample size problem by averaging and incorporating over multiple classification models to reduce the potential for overfitting the training data [ 16].
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The approach extends a combined rule and case-based knowledge acquisition technique known as Multiple Classification Ripple Down Rules to allow multiple users to collaboratively view, define and refine a knowledge base over time and space.
Race classification is a multiple classification that, as a rule, identifies Caucasians, Asians and Blacks.
Freight trips have been extrapolated using a Multiple Classification Analysis (MCA) model.
The noncompatibilist is open to multiple classification schemes answerable to different pragmatic interests in classifying.
Hence, the final classification decision can be made by combining these multiple classification decisions to obtain a better performance.
Identifying types of J-proteins is a multiple classification problem.
A classification model is usually obtained by trying multiple classification algorithms [ 28, 29].
These numbers slightly understate the rate of classification changes because each report may be affected by multiple classification changes.
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