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(a) Unlike most existing studies that are based on supervised feature selection, our study applies unsupervised feature selection.
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g c ∈ C } In the methods mentioned above, a feature is recursively added to the chosen feature set based on supervised learning and the similarity measures.
As feature extraction based on supervised Nonnegative Matrix Factorization (NMF) has been proposed in automatic speech recognition for enhanced robustness, we introduce and evaluate different kinds of NMF-based features for emotion recognition.
Based on supervised learning and similarity measurements, we propose a Recursive Feature Addition (RFA), recursively employ supervised learning to obtain the highest training accuracy and add a subsequent gene based on the similarity between the chosen features and the candidates to minimize the redundancy within the feature set.
To exploit the information redundancy that exists among the huge number of variables and improve classification accuracy of microarray data, we propose a gene selection method, Recursive Feature Addition (RFA), which is based on supervised learning and similarity measures.
These classifiers generate a decision boundary that optimally partitions the feature space into a polyp class and a false-positive class based on supervised learning.
Most of the existing tumor classification approaches are based on supervised learning, such as support vector machines or decision trees, which aimed at identifying genetic features to distinguish two or more known tumor (sub- types [ 49, 50].
In contrast, our approach is based on supervised machine learning.
Based on supervised trial value, the supervised trial median residue (STMR) in tomatoes was 0.069 mg/kg (Table 2).
The process itself relies purely on the tweet (text) which is segmented based on supervised learning.
Context recognition algorithms based on supervised and unsupervised learning methods primarily use probabilistic and statistical reasoning.
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