47 citations · 61 across the 4 of their papers we have counts for
4 papers
Multi-label Causal Variable Discovery: Learning Common Causal Variables and Label-specific Causal Variables
Xingyu Wu, Bingbing Jiang, Yan Zhong +1
Causal variables in Markov boundary (MB) have been widely applied in extensive single-label tasks. While few researches focus on the causal variable discovery in multi-label data d…
Probabilistic Classification Vector Machine for Multi-Class Classification
Shengfei Lyu, Xing Tian, Yang Li +2
The probabilistic classification vector machine (PCVM) synthesizes the advantages of both the support vector machine and the relevant vector machine, delivering a sparse Bayesian s…
Multiple Kernel -Means Clustering by Selecting Representative Kernels
Yaqiang Yao, Huanhuan Chen
To cluster data that are not linearly separable in the original feature space, -means clustering was extended to the kernel version. However, the performance of kernel -means…
Probabilistic Feature Selection and Classification Vector Machine
Bingbing Jiang, Chang Li, Maarten de Rijke +2
Sparse Bayesian learning is a state-of-the-art supervised learning algorithm that can choose a subset of relevant samples from the input data and make reliable probabilistic predic…