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Experimental results show that a cascade classifier using weak classifiers selected by the proposed method has a higher detection performance.
While these methods achieve higher detection performance, the weight update is heuristic, making it difficult to predict performance prior to in-field use.
In the Caltech Faces, the AdaBoost HL cascade shows higher detection performance than our MS-EGM, which wins against the AdaBoost HL cascade, in terms of process speed.
Comparative experimental results on the BH-BC database show that our approach can get a much higher detection performance than the state-of-the-art classifiers (Support Vector Machine and Adaboost).
We demonstrate using several synthetic and real datasets that our proposed scheme achieves a higher detection performance with a significant reduction in communication overhead in the network compared to centralised and existing schemes.
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The proposed process achieved high detection performance with less error rate of 0.42% in detecting QRS complex.
A strong classifier is a classifier which is supposed to have high detection performance.
To achieve the highest detection performance, there is an optimum choice of the sensor number.
We expect that, even without them, the combination of GMMs and SVMs will have high detection performance.
The human detection is then guided by the candidate windows, offering high detection performance even when the detection task terminates prior to completion.
The cluster the target located in serves as the root cluster and the other clusters serve as the branch or leaf clusters, in this way, the root cluster can achieve the highest detection performance.
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