Proceedings Article10.1145/1390156.1390258
Bayesian multiple instance learning: automatic feature selection and inductive transfer
Vikas C. Raykar,Balaji Krishnapuram,Jinbo Bi,Murat Dundar,R. Bharat Rao +4 more
- 05 Jul 2008
- pp 808-815
TL;DR: A novel Bayesian multiple instance learning algorithm that automatically identifies the relevant feature subset, and utilizes inductive transfer when learning multiple (conceptually related) classifiers.
read more
Abstract: We propose a novel Bayesian multiple instance learning (MIL) algorithm This algorithm automatically identifies the relevant feature subset, and utilizes inductive transfer when learning multiple (conceptually related) classifiers Experimental results indicate that the proposed MIL method is more accurate than previous MIL algorithms and selects a much smaller set of useful features Inductive transfer further improves the accuracy of the classifier as compared to learning each task individually
read more
Chat with Paper
AI Agents for this Paper
Find similar papers on Google Scholar, PubMed and Arxiv
Write a critical review of this paper
Analyze citations of this paper to find unaddressed research gaps
Citations
A Survey on Transfer Learning
Sinno Jialin Pan,Qiang Yang +1 more
TL;DR: The relationship between transfer learning and other related machine learning techniques such as domain adaptation, multitask learning and sample selection bias, as well as covariate shift are discussed.
An embarrassingly simple approach to zero-shot learning
Bernardino Romera-Paredes,Philip H. S. Torr +1 more
- 06 Jul 2015
TL;DR: This paper describes a zero-shot learning approach that can be implemented in just one line of code, yet it is able to outperform state of the art approaches on standard datasets.
•Proceedings Article
Attention-based Deep Multiple Instance Learning
Maximilian Ilse,Jakub M. Tomczak,Max Welling +2 more
- 03 Jul 2018
TL;DR: In this paper, a neural network-based permutation-invariant aggregation operator is proposed to learn the Bernoulli distribution of the bag label, where the bag-label probability is fully parameterized by neural networks.
Transfer learning using computational intelligence
TL;DR: This paper systematically examines computational intelligence-based transfer learning techniques and clusters related technique developments into four main categories and provides state-of-the-art knowledge that will directly support researchers and practice-based professionals to understand the developments in computational Intelligence- based transfer learning research and applications.
Multiple instance learning: A survey of problem characteristics and applications
TL;DR: A comprehensive survey of the characteristics which define and differentiate the types of MIL problems is provided, providing insight on how the problem characteristics affect MIL algorithms, recommendations for future benchmarking and promising avenues for research.
765
References
Multitask Learning
Rich Caruana
- 01 Jul 1997
TL;DR: Multi-task Learning (MTL) as mentioned in this paper is an approach to inductive transfer that improves generalization by using the domain information contained in the training signals of related tasks as an inductive bias.
Sparse bayesian learning and the relevance vector machine
TL;DR: It is demonstrated that by exploiting a probabilistic Bayesian learning framework, the 'relevance vector machine' (RVM) can derive accurate prediction models which typically utilise dramatically fewer basis functions than a comparable SVM while offering a number of additional advantages.
Solving the multiple instance problem with axis-parallel rectangles
TL;DR: Three kinds of algorithms that learn axis-parallel rectangles to solve the multiple instance problem are described and compared, giving 89% correct predictions on a musk odor prediction task.
3.2K
•Proceedings Article
Support Vector Machines for Multiple-Instance Learning
Stuart Andrews,Ioannis Tsochantaridis,Thomas Hofmann +2 more
- 01 Jan 2002
TL;DR: The proposed extensions of the Support Vector Machine learning approach lead to mixed integer quadratic programs that can be solved heuristic ally and a generalization of SVMs makes a state-of-the-art classification technique, including non-linear classification via kernels, available to an area that up to now has been largely dominated by special purpose methods.