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Cultural modeling aims at developing behavioral models of groups and analyzing the impact of culture factors on group behavior using computational methods. Machine learning methods in particular classification, play a central role in such applications. In modeling cultural data, it is expected that standard classifiers yield good performance under the assumptions that class distribution is balanced...
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...
Detection of interacting SNPs predictive of complex disease will help identify individuals at high risk, make personalized treatment possible, and provide novel insights into the pathophysiology of the conditions in question. Although the interaction effect of multi-locus SNPs is widely expected, the existing strategies have limited power in detecting SNPs with interaction effects. This paper presents...
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...
We consider ensemble classification when there is no common labeled data for designing the function which aggregates classifier decisions. In recent work, we dubbed this problem distributed ensemble classification, addressing e.g. when local classifiers are trained on different (e.g. proprietary, legacy) databases or operate on different sensing modalities. Typically, fixed (untrained) rules of classifier...
Multilayer perceptrons offer an integrated procedure for feature extraction and Bayes classification by learning the decision boundary. Its feedforward autoassociative architecture can also be used to construct subspaces in a supervised or unsupervised model [A.K. Jain et al., 2000]. On the other hand, multiclass linear discriminant analysis provides a multivariate prediction by estimating the density...
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