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A Note on Learning from Multiple-Instance Examples
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A framework for multiple-instance learning
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Approximating hyper-rectangles: learning and pseudorandom sets
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Multiple-Instance Learning of Real-Valued Data
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Content-Based Image Retrieval Using Multiple-Instance Learning
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On Learning From Multi-Instance Examples: Empirical Evaluation of a Theoretical Approach
ICML '97 Proceedings of the Fourteenth International Conference on Machine Learning
A Probabilistic Analysis of the Rocchio Algorithm with TFIDF for Text Categorization
ICML '97 Proceedings of the Fourteenth International Conference on Machine Learning
Multiple-Instance Learning for Natural Scene Classification
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Learning Structurally Indeterminate Clauses
ILP '98 Proceedings of the 8th International Workshop on Inductive Logic Programming
Attribute-Value Learning Versus Inductive Logic Programming: The Missing Links (Extended Abstract)
ILP '98 Proceedings of the 8th International Workshop on Inductive Logic Programming
Solving the Multiple-Instance Problem: A Lazy Learning Approach
ICML '00 Proceedings of the Seventeenth International Conference on Machine Learning
Image Database Retrieval with Multiple-Instance Learning Techniques
ICDE '00 Proceedings of the 16th International Conference on Data Engineering
Learning from ambiguity
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On the relation between multi-instance learning and semi-supervised learning
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Incremental development of CBR strategies for computing project cost probabilities
Advanced Engineering Informatics
Multi-objective Genetic Programming for Multiple Instance Learning
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Multi-instance genetic programming for web index recommendation
Expert Systems with Applications: An International Journal
Multi-instance clustering with applications to multi-instance prediction
Applied Intelligence
Predicting types of protein-protein interactions using a multiple-instance learning model
JSAI'06 Proceedings of the 20th annual conference on New frontiers in artificial intelligence
Multiple instance learning with genetic programming for web mining
IWANN'07 Proceedings of the 9th international work conference on Artificial neural networks
Multiple Instance Learning with Multiple Objective Genetic Programming for Web Mining
Applied Soft Computing
G3P-MI: A genetic programming algorithm for multiple instance learning
Information Sciences: an International Journal
Predicting MHC-II Binding Affinity Using Multiple Instance Regression
IEEE/ACM Transactions on Computational Biology and Bioinformatics (TCBB)
Recognition of adult images, videos, and web page bags
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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
Locating regions of interest in CBIR with multi-instance learning techniques
AI'05 Proceedings of the 18th Australian Joint conference on Advances in Artificial Intelligence
An efficient parallel neural network-based multi-instance learning algorithm
The Journal of Supercomputing
HyDR-MI: A hybrid algorithm to reduce dimensionality in multiple instance learning
Information Sciences: an International Journal
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The Journal of Machine Learning Research
Multiple instance learning based on positive instance selection and bag structure construction
Pattern Recognition Letters
Fundamenta Informaticae - Concurrency, Specification and Programming
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In multi-instance learning, the training set comprises labeled bags that are composed of unlabeled instances, and the task is to predict the labels of unseen bags. In this paper, a web mining problem, i.e. web index recommendation, is investigated from a multi-instance view. In detail, each web index page is regarded as a bag, while each of its linked pages is regarded as an instance. A user favoring an index page means that he or she is interested in at least one page linked by the index. Based on the browsing history of the user, recommendation could be provided for unseen index pages. An algorithm named Fretcit-kNN, which employs the Minimal Hausdorff distance between frequent term sets and utilizes both the references and citers of an unseen bag in determining its label, is proposed to solve the problem. Experiments show that in average the recommendation accuracy of Fretcit-kNN is 81.0% with 71.7% recall and 70.9% precision, which is significantly better than the best algorithm that does not consider the specific characteristics of multi-instance learning, whose performance is 76.3% accuracy with 63.4% recall and 66.1% precision.