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A highly resolved tree of phenotypes (TOP) derived from genomic data reveals important relationships between heterogeneous diseases at molecular level. We propose a stability analysis guided learning method that produces a reproducible yet non-binary TOP using high-dimensional finite sample size genomic data. Experimental results show the superior capability of the proposed method in learning TOP...
This paper presents a novel kernel density estimation approach to vehicle trajectory learning and motion analysis. The framework comprises a training stage and a testing stage. In the training stage, vehicle trajectories are first clustered by the hierarchical spectral clustering method. Then, through the proposed kernel density estimation approach, the average kernel density of one point on a trajectory...
In this paper, an self-organizing TSK-type fuzzy neural network is proposed for predicting the short-term traffic flow. The proposed fuzzy neural network is adaptively organized from the collected short-term traffic flow data. The whole process is divided into two stage, i.e., structure identification and parameter learning. In structure identification, the mean shift clustering algorithm performs...
A novel hybrid learning algorithm for designing a TSK-type recurrent fuzzy neural network (RFNN) is proposed in this paper. The whole designing process includes two stages, i.e., structure identification and parameter optimization. The structure identification includes mean shift clustering (MSC) and mean firing strength (MFS). The MSC is used to partition the input space and the mean firing strength...
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