Bayesian multiple instance learning: automatic feature selection and inductive transfer

By Vikas Chandrakant Raykar, Balaji Krishnapuram, Jinbo Bi, Murat Dundar, R. B. Rao. 2008

160 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Computer Vision and Pattern Recognition

How researchers see this paper

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

In Artificial Intelligence

1. Qiang Yang
2. Dacheng Tao
3. Philip H. S. Torr
4. Mohammad Rostami
5. Jianmin Wang
6. Zhi-Hua Zhou
7. Mingsheng Long
8. Bernt Schiele

In Computer Vision and Pattern Recognition

1. Luc Van Gool
2. Dacheng Tao
3. Philip H. S. Torr
4. Ming-Hsuan Yang
5. Trevor Darrell
6. Alan L. Yuille
7. Bernt Schiele
8. Yi Yang

Citations in the corpus

• An embarrassingly simple approach to zero-shot learning (2015, background): “machine learning problems, and has received several names, such as learning to learn [23] or inductive transfer [7, 31, 33].”
• Attention-based Deep Multiple Instance Learning (2018, neutral): “, 2006), and combining responses of an instance-level classifier (Ramon & De Raedt, 2000; Raykar et al., 2008; Zhang et al., 2006).”
• Multiple Instance Transfer Learning (2009, methodology): “We have also conducted comprehensive performance evaluations by testing our method and comparing it with Multiple Instance SVM (MISVM)[2], and the Bayesian Multiple Instance Multiple Task Learning method - BMIMT [11].”
• Multi-Instance Dimensionality Reduction (2010, methodology): “Raykar et al. (2008) have studied multi-instance feature selection using Bayesian method which automatically considers the feature relevance.”
• HyDR-MI: A hybrid algorithm to reduce dimensionality in multiple instance learning (2013, methodology): “(33) proposed a Bayesian MIL algorithm that uses feature selection.”
• Transfer learning using computational intelligence: A survey (2015, methodology): “[85] presented a novel Bayesian multiple instance learning (MIL) algorithm, which performs feature selection and classifier construction simultaneously.”
• A Survey on Transfer Learning (2010, background): “In [81], Raykar et al. proposed a novel Bayesian multipleinstance learning algorithm, which can automatically identify the relevant feature subset and use inductive transfer for learning multiple, but conceptually related, classifiers, for computer aided design (CAD).”
• Improving Computer-aided Detection using Convolutional Neural Networks and Random View Aggregation (2015, result): “This type of cascade classification is similar in spirit to our presented second tier deep classifier (ConvNet), but uses state-of-the-art shallow classifiers (libSVM [47] and sparse linear fusion via the Relevance Vector Machine [48]).”
• Weakly supervised histopathology cancer image segmentation and classification (2014, background): “, 2008, 2006, 2011), pulmonary emboli validation (Raykar et al., 2008), and pathology slide classification (Dundar et al.”