Probabilistic reasoning in intelligent systems: networks of plausible inference
Probabilistic reasoning in intelligent systems: networks of plausible inference
Solving the multiple instance problem with axis-parallel rectangles
Artificial Intelligence
A Note on Learning from Multiple-Instance Examples
Machine Learning - Special issue on the ninth annual conference on computational theory (COLT '96)
Making large-scale support vector machine learning practical
Advances in kernel methods
A framework for multiple-instance learning
NIPS '97 Proceedings of the 1997 conference on Advances in neural information processing systems 10
SSVM: A Smooth Support Vector Machine for Classification
Computational Optimization and Applications
Inductive Logic Programming: Techniques and Applications
Inductive Logic Programming: Techniques and Applications
Learning Logical Definitions from Relations
Machine Learning
Content-Based Image Retrieval Using Multiple-Instance Learning
ICML '02 Proceedings of the Nineteenth International Conference on Machine Learning
ICML '02 Proceedings of the Nineteenth International Conference on Machine Learning
Multiple-Instance Learning for Natural Scene Classification
ICML '98 Proceedings of the Fifteenth International Conference on Machine Learning
Solving the Multiple-Instance Problem: A Lazy Learning Approach
ICML '00 Proceedings of the Seventeenth International Conference on Machine Learning
Learning from ambiguity
SVM-based generalized multiple-instance learning via approximate box counting
ICML '04 Proceedings of the twenty-first international conference on Machine learning
A regularization framework for multiple-instance learning
ICML '06 Proceedings of the 23rd international conference on Machine learning
Bellwether analysis: predicting global aggregates from local regions
VLDB '06 Proceedings of the 32nd international conference on Very large data bases
MILES: Multiple-Instance Learning via Embedded Instance Selection
IEEE Transactions on Pattern Analysis and Machine Intelligence
Multiple instance learning for sparse positive bags
Proceedings of the 24th international conference on Machine learning
On the relation between multi-instance learning and semi-supervised learning
Proceedings of the 24th international conference on Machine learning
Optimistic pruning for multiple instance learning
Pattern Recognition Letters
Bayesian multiple instance learning: automatic feature selection and inductive transfer
Proceedings of the 25th international conference on Machine learning
A Multiple Instance Learning Strategy for Combating Good Word Attacks on Spam Filters
The Journal of Machine Learning Research
Graph-based multiple-instance learning for object-based image retrieval
MIR '08 Proceedings of the 1st ACM international conference on Multimedia information retrieval
Improving Promoter Prediction Using Multiple Instance Learning
AI '08 Proceedings of the 21st Australasian Joint Conference on Artificial Intelligence: Advances in Artificial Intelligence
Learning Curves for the Analysis of Multiple Instance Classifiers
SSPR & SPR '08 Proceedings of the 2008 Joint IAPR International Workshop on Structural, Syntactic, and Statistical Pattern Recognition
Bellwether analysis: Searching for cost-effective query-defined predictors in large databases
ACM Transactions on Knowledge Discovery from Data (TKDD)
Multi-instance learning by treating instances as non-I.I.D. samples
ICML '09 Proceedings of the 26th Annual International Conference on Machine Learning
Multi-instance clustering with applications to multi-instance prediction
Applied Intelligence
A Convex Method for Locating Regions of Interest with Multi-instance Learning
ECML PKDD '09 Proceedings of the European Conference on Machine Learning and Knowledge Discovery in Databases: Part II
Learning to connect language and perception
AAAI'08 Proceedings of the 23rd national conference on Artificial intelligence - Volume 3
MICCLLR: Multiple-Instance Learning Using Class Conditional Log Likelihood Ratio
DS '09 Proceedings of the 12th International Conference on Discovery Science
The Knowledge Engineering Review
G3P-MI: A genetic programming algorithm for multiple instance learning
Information Sciences: an International Journal
Asking generalized queries to ambiguous oracle
ECML PKDD'10 Proceedings of the 2010 European conference on Machine learning and knowledge discovery in databases: Part I
Entropy and margin maximization for structured output learning
ECML PKDD'10 Proceedings of the 2010 European conference on Machine learning and knowledge discovery in databases: Part III
Speeding up and boosting diverse density learning
DS'10 Proceedings of the 13th international conference on Discovery science
Predicting MHC-II Binding Affinity Using Multiple Instance Regression
IEEE/ACM Transactions on Computational Biology and Bioinformatics (TCBB)
ShotTagger: tag location for internet videos
Proceedings of the 1st ACM International Conference on Multimedia Retrieval
Bag dissimilarities for multiple instance learning
SIMBAD'11 Proceedings of the First international conference on Similarity-based pattern recognition
Multi-instance multi-label learning
Artificial Intelligence
Unsupervised multiple-instance learning for functional profiling of genomic data
ECML'06 Proceedings of the 17th European conference on Machine Learning
Multiple-Instance learning via random walk
ECML'06 Proceedings of the 17th European conference on Machine Learning
HyDR-MI: A hybrid algorithm to reduce dimensionality in multiple instance learning
Information Sciences: an International Journal
Multiple-instance learning as a classifier combining problem
Pattern Recognition
ECCV'12 Proceedings of the 12th European conference on Computer Vision - Volume Part IV
An informatics-based approach to object tracking for distributed live video computing
Multimedia Tools and Applications
| Hi-index | 0.00 |
We empirically study the relationship between supervised and multiple instance (MI) learning. Algorithms to learn various concepts have been adapted to the MI representation. However, it is also known that concepts that are PAC-learnable with one-sided noise can be learned from MI data. A relevant question then is: how well do supervised learners do on MI data? We attempt to answer this question by looking at a cross section of MI data sets from various domains coupled with a number of learning algorithms including Diverse Density, Logistic Regression, nonlinear Support Vector Machines and FOIL. We consider a supervised and MI version of each learner. Several interesting conclusions emerge from our work: (1) no MI algorithm is superior across all tested domains, (2) some MI algorithms are consistently superior to their supervised counterparts, (3) using high false-positive costs can improve a supervised learner's performance in MI domains, and (4) in several domains, a supervised algorithm is superior to any MI algorithm we tested.