A new parametric model was developed for predicting cut point of hydraulic classifiers. The model directly uses operating parameters including pulp flowrate, feed particle size characteristics, pulp solids content, solid density and particles retention time in the classification chamber and also covers uncontrollable errors using calibration constants. The model applicability was first verified...

Machine learning has achieved dramatic success in a broad spectrum of applications. Its interplay with quantum physics may lead to unprecedented perspectives for both fundamental research and commercial applications, giving rise an emergent frontier machine learning. Along this line, classifiers, which are devices that aim solve classification problems learning, have attracted tremendous attent...

Several important models of machine learning algorithms have been successfully generalized to the quantum world, with potential speedup training classical classifiers and applications data analytics in physics that can be implemented on near future computers. However, noise is a major obstacle practical implementation learning. In this work, we define formal framework for robustness verificatio...

multiple people detection and tracking is a challenging task in real-world crowded scenes. in this paper, we have presented an online multiple people tracking-by-detection approach with a single camera. we have detected objects with deformable part models and a visual background extractor. in the tracking phase we have used a combination of support vector machine (svm) person-specific classifie...