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)
A framework for multiple-instance learning
NIPS '97 Proceedings of the 1997 conference on Advances in neural information processing systems 10
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
Selective Propositionalization for Relational Learning
PKDD '99 Proceedings of the Third European Conference on Principles of Data Mining and Knowledge Discovery
MILES: Multiple-Instance Learning via Embedded Instance Selection
IEEE Transactions on Pattern Analysis and Machine Intelligence
Multi-objective Genetic Programming for Multiple Instance Learning
ECML '07 Proceedings of the 18th European conference on Machine Learning
Grammar guided genetic programming for multiple instance learning: an experimental study
Proceedings of the 12th annual conference on Genetic and evolutionary computation
The Knowledge Engineering Review
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
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
Speeding up and boosting diverse density learning
DS'10 Proceedings of the 13th international conference on Discovery science
Online multiple instance boosting for object detection
Neurocomputing
Adaptive kernel diverse density estimate for multiple instance learning
MLDM'11 Proceedings of the 7th international conference on Machine learning and data mining in pattern recognition
Unsupervised multiple-instance learning for functional profiling of genomic data
ECML'06 Proceedings of the 17th European conference on Machine Learning
Mining chemical compound structure data using inductive logic programming
AM'03 Proceedings of the Second international conference on Active Mining
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
Beyond trees: adopting MITI to learn rules and ensemble classifiers for multi-instance data
AI'11 Proceedings of the 24th international conference on Advances in Artificial Intelligence
Multiple instance learning for classifying students in learning management systems
Expert Systems with Applications: An International Journal
Constructing target concept in multiple instance learning using maximum partial entropy
MLDM'12 Proceedings of the 8th international conference on Machine Learning and Data Mining in Pattern Recognition
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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In recent work, Dietterich et al. (1997) have presented the problem of supervised multiple-instance learning and how to solve it by building axis-parallel rectangles. This problem is encountered in contexts where an object may have different possible alternative configurations, each of which is described by a vector. This paper introduces the multiple-part problem, which is related to the multiple-instance problem, and shows how it can be solved using the multiple-instance algorithms. These two so-called "multiple" problems could play a key role both in the development of efficient algorithms for learning the relations between the activity of a structured object and its structural properties and in relational learning. This paper analyzes and tries to clarify multiple-problem solving. It goes on to propose multiple-instance extensions of classical learning algorithms to solve multiple-problems by learning multiple-decision trees (ID3-MI) and multiple-decision rules (RIPPERMI). In particular, it suggests a new multiple-instance entropy function and a multiple-instance coverage function. Finally, it successfully applies the multiple-part framework on the well-known mutagenesis prediction problem.