Probabilistic reasoning in intelligent systems: networks of plausible inference
Probabilistic reasoning in intelligent systems: networks of plausible inference
Learning internal representations by error propagation
Parallel distributed processing: explorations in the microstructure of cognition, vol. 1
Solving the multiple instance problem with axis-parallel rectangles
Artificial Intelligence
Machine Learning - Special issue on the ninth annual conference on computational theory (COLT '96)
A Note on Learning from Multiple-Instance Examples
Machine Learning - Special issue on the ninth annual conference on computational theory (COLT '96)
A framework for multiple-instance learning
NIPS '97 Proceedings of the 1997 conference on Advances in neural information processing systems 10
Approximating hyper-rectangles: learning and pseudorandom sets
Journal of Computer and System Sciences - Fourteenth ACM SIGACT-SIGMOD-SIGART symposium on principles of database systems
Multiple-Instance Learning of Real-Valued Data
ICML '01 Proceedings of the Eighteenth International Conference on Machine Learning
On Learning From Multi-Instance Examples: Empirical Evaluation of a Theoretical Approach
ICML '97 Proceedings of the Fourteenth International Conference on Machine Learning
Multiple-Instance Learning for Natural Scene Classification
ICML '98 Proceedings of the Fifteenth International Conference on Machine Learning
ICML '01 Proceedings of the Eighteenth International Conference on Machine Learning
Real-Valued Multiple-Instance Learning with Queries
ALT '01 Proceedings of the 12th International Conference on Algorithmic Learning Theory
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
Adapting RBF Neural Networks to Multi-Instance Learning
Neural Processing Letters
Ensembles of multi-instance neural networks
Intelligent information processing II
Solving multi-instance problems with classifier ensemble based on constructive clustering
Knowledge and Information Systems - Special Issue on Mining Low-Quality Data
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
Amulti-instance model for software quality estimation in OO systems
ICNC'09 Proceedings of the 5th international conference on Natural computation
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
Multi-instance multi-label learning
Artificial Intelligence
Reducing dimensionality in multiple instance learning with a filter method
HAIS'10 Proceedings of the 5th international conference on Hybrid Artificial Intelligence Systems - Volume Part II
Multiple instance learning for classifying students in learning management systems
Expert Systems with Applications: An International Journal
HyDR-MI: A hybrid algorithm to reduce dimensionality in multiple instance learning
Information Sciences: an International Journal
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Multi-instance learning is regarded as a new learning framework where the training examples are bags composed of instances without labels, and the task is to predict the labels of unseen bags through analyzing the training bags with known labels. Recently, a multi-instance neural network BP-MIP was proposed. In this paper, BP-MIP is improved through adopting two different feature selection techniques, i.e. feature scaling with Diverse Density and feature reduction with principal component analysis. In detail, before feature vectors are fed to a BP-MIP neural network, they are scaled by the feature weights found by running Diverse Density on the training data, or projected by a linear transformation matrix formed by principal component analysis. Experiments show that these feature selection mechanisms can significantly improve the performance of BP-MIP.