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Although human immunology is generally not a topic included in the junior high school science standard curriculum in Taiwan, we really hope that the game could be used for students of different ages to provide students with multiple learning approaches.
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We combined multiple perceptual learning approaches that have individually contributed to increasing the speed, magnitude and generality of learning into a perceptual-learning based video-game.
This method can be easily extended to a supervised scenario, and it has also been reported that it can outperform the conventional multiple kernel learning approaches.
Multiple machine learning approaches were used, listed in the Materials and Methods.
We introduce a family of kernels capturing the similarity of fragmentation trees, and combine these kernels using recently proposed multiple kernel learning approaches.
The regional contribution to the decision function implemented by a multiple kernel learning approach is most prominent in the right rolandic operculum and the rSTG with a predictive power of 72%.
In the current study, multiple supervised machine learning approaches are used to classify melt pools to predict porosity in a part.
The MIL algorithm is a classical multiple-instance learning approach for target tracking.
Thus, one advantage of machine learning approaches using multiple attributes is that they can discover multiple combinations of attributes that suggest redundancy or non-redundancy.
Lu et al. proposed a Localized Multiple Kernel Metric Learning approach to classify images taken from varying viewpoints or under varying illuminations [28].
The work presented in Chattopadhyay [14] is a multiple source transfer learning approach that calculates the source weights as a function of conditional probability differences between the source and target domains attempting to give the most related sources the highest weights.
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