Abstract:
Multi-instance learning originates from the investigation on drug activity prediction, where the task is to predict whether an unseen molecule could be used to make some drug. Such a problem is difficult because a molecule may have many alternative shapes with low energy, yet only one of those shapes may be responsible for the qualification of the molecule to make the drug. Because of its unique characteristics and extensive existence, multiinstance learning is regarded as a new machine learning framework parallel to supervised learning, unsupervised learning, and reinforcement learning. In this paper, an open problem of this area is addressed. That is, a popular neural network algorithm is adapted for multi-instance learning through employing a specific error function. Experiments show that the adapted algorithm achieves good result on the drug activity prediction data. 1.
Citations
|
2140
|
Learning Internal Representations by Error Propagation
– Rumelhart, Hinton, et al.
- 1986
|
|
2138
|
UCI Repository of Machine Learning Databases
– Merz, Murphy
- 1996
|
|
115
|
Solving the multiple-instance problem with axis-parallel rectangles
– Dietterich, Lathrop, et al.
- 1997
|
|
85
|
Multiple-instance learning for natural scene classification
– Maron, Ratan
- 1998
|
|
69
|
A framework for multiple-instance learning
– Maron, Lozano-Perez
- 1998
|
|
43
|
On learning from multi-instance examples: Empirical evaluation of a theoretical approach
– Auer
- 1997
|
|
32
|
Learning from Ambiguity
– Maron
- 1998
|
|
30
|
A note on learning from multiple-instance examples
– Blum, Kalai
- 1998
|
|
26
|
Solving the multiple-instance problem: A lazy learning approach
– Wang, Zucker
- 2000
|
|
24
|
Image database retrieval with multiple-instance learning techniques
– Yang, Lozano-Perez
- 2000
|
|
23
|
Multiple instance regression
– Ray, Page
- 2001
|
|
21
|
PAC learning axis-aligned rectangles with respect to product distributions from multiple-instance examples
– Long, Tan
- 1998
|
|
20
|
Solving multiple-instance and multiple-part learning problems with decision trees and decision rules. Application to the mutagenesis problem
– Zucker, Chevaleyre
- 2000
|
|
20
|
Learning Single and Multiple Instance Decision Trees for Computer Security Applications
– Ruffo
- 2000
|
|
18
|
Multiple-instance learning of real-valued data
– Amar, Dooly, et al.
- 2001
|
|
1
|
Stedman’s medical dictionary
– unknown authors
- 1995
|
