Marginalized Multi-Instance Kernels

By James T. Kwok, Pak-Ming Cheung. 2007

66 citations recorded by the source corpus.

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

1. Zhi-Hua Zhou
2. Min-Ling Zhang
3. Yu-Feng Li
4. Dan Zhang
5. Sheng-Jun Huang
6. Wentao Zhu
7. Xiaohui S. Xie
8. Luo Si

Citations in the corpus

• Deep Multi-instance Networks with Sparse Label Assignment for Whole Mammogram Classification (2016, methodology): “Kwok and Cheung employed marginalized kernel to solve the instance label ambiguity in multi-instance learning [14].”
• Multi-instance learning by treating instances as non-I.I.D. samples (2008, methodology): “Later, Kwok and Cheung [15] designed marginalized multi-instance kernels by incorporating generative model into the kernel design.”
• Multi-instance multi-label learning (2008, methodology): “To name a few, Diverse Density [45] and Em-dd [83], k-nearest neighbor algorithms Citation-knn and Bayesian-knn [67], decision tree algorithms Relic [54] and Miti [9], neural network algorithms Bp-mip and extensions [77,90] and Rbf-mip [78], rule learning algorithm Ripper-mi [20], support vector mac”
• Maximum Margin Multiple Instance Clustering With Applications to Image and Text Clustering (2011, background): “Later, Kwok and Cheung [19] extended their work by proposing a marginalized MI kernel and converting the MIL problem from an incomplete data problem to a complete data problem.”