Scalable Algorithms for Multi|Instance Learning

Scalable Algorithms for Multi-Instance Learning - IEEE Xplore

Scalable Algorithms for Multi-Instance Learning. Abstract: Multi-instance learning (MIL) has been widely applied to diverse applications ...

Scalable Algorithms for Multi-Instance Learning - LAMDA

Thanks to the low computational cost in the mapping step and the scalability of linear classifiers, miVLAD and miFV can handle large scale MIL data efficiently ...

Scalable Multi-instance Learning - IEEE Xplore

However, most existing MIL algorithms can only handle small-or moderate-sized data. In order to deal with the large scale problems in MIL, we propose an ...

Scalable Multiple Instance Learning - Xiu-Shen Wei

However, most existing MIL algorithms can only handle small- or moderate-sized data. In order to deal with large scale MIL problems, we propose miVLAD and miFV, ...

(PDF) Scalable Multi-instance Learning - ResearchGate

However, most existing MIL algorithms can only handle small-or moderate-sized data. In order to deal with the large scale problems in MIL, we propose an ...

Scalable Multi-Instance Learning

In this paper, we propose an efficient and scalable MIL algorithm which is miFV (multi-instance learning based on the Fisher Vector representation). In miFV ...

A Framework for Multiple-Instance Learning

... scaling of the individual features by finding the scalings that maximize Diverse Density. The algorithm returns both a location and a scaling vector , where ...

Scalable Multi-instance Learning | Proceedings of the 2014 IEEE ...

However, most existing MIL algorithms can only handle small-or moderate-sized data. In order to deal with the large scale problems in MIL, we propose an ...

Scalable Multi-agent Reinforcement Learning Algorithm for Wireless ...

Scalable Multi-agent Reinforcement Learning. Algorithm for Wireless Networks. Fenghe Hu, Yansha Deng, and A. Hamid Aghvami. Abstract.

Are Multiple Instance Learning Algorithms Learnable for Instances?

These results demonstrate the scalability of the proposed framework and provide a theoretical basis for future research in MD-MIL.

Scalable hierarchical multitask learning algorithms for conversion ...

Argyriou, T. Evgeniou, and M. Pontil. Convex multi-task feature learning. Machine Learning, 73(3):243--272, 2008.

Robust Self-Supervised Multi-Instance Learning with Structure ...

Scalable algorithms for multi-instance learning. IEEE Transactions on Neural. Networks and Learning Systems, 28(4): 975–987. Yager, R. R. 1988. On ordered ...

Robust bag classification approach for multi-instance learning via ...

Specifically, the proposed algorithm uses a subspace fuzzy clustering approach to compute instance selection probabilities, selects essential ...

Multiple instance learning - Wikipedia

... scaling vector. This way, if every positive bag ... "Multiple instance learning: algorithms and applications. ... "Multi-instance multi-label learning with ...

Multi-Instance Learning with Key Instance Shift - IJCAI

Algorithm 2 Learning algorithm for multi-class model MD. Input ... Scalable algorithms for multi-instance learning. IEEE Transactions on Neural ...

Multi-Instance Learning with Any Hypothesis Class

For instance, as we show below, a generic PAC-learning algorithm can be derived for a large class of MIL problems with different hypothesis classes. Other ...

Scalable Algorithms for Multiple Network Alignment | Vol. 43, No. 5

We perform a case study on anonymized data from a collaboration network, where we show that aligning anonymized triplets of egonets can identify ...

EM-DD: An Improved Multiple-Instance Learning Technique

The algorithm is applied to both boolean and real-value labeled data and the results are compared with corresponding MI learning algorithms from previous work.

A Review of Multi-Instance Learning Assumptions - of James Foulds

A Review of Multi-Instance Learning Assumptions. 13 where σ is a scaling factor, which is a parameter to the algorithm. The relationship between the ...

Review Exploring Multiple Instance Learning (MIL): A brief survey

We provide applications of Multiple instance learning in various domains. •. We discuss how existing supervised learning algorithms are modified ...