The cascade-correlation learning architecture
Advances in neural information processing systems 2
Hypothesis-Driven Constructive Induction in AQ17-HCI: A Method and Experiments
Machine Learning - Special issue on evaluating and changing representation
Artificial Intelligence
Selection of relevant features and examples in machine learning
Artificial Intelligence - Special issue on relevance
Using Abstrips Abstractions -- Where do WeStand?
Artificial Intelligence Review
Phase Transitions in Relational Learning
Machine Learning
Models of perceptual learning in vernier hyperacuity
Neural Computation
A semantic theory of abstractions
IJCAI'95 Proceedings of the 14th international joint conference on Artificial intelligence - Volume 1
Perspectives on Cognitive Informatics
ICCI '03 Proceedings of the 2nd IEEE International Conference on Cognitive Informatics
Multistrategy Operators for Relational Learning and Their Cooperation
Fundamenta Informaticae
Multistrategy Operators for Relational Learning and Their Cooperation
Fundamenta Informaticae
| Hi-index | 0.00 |
This paper deals with the possible benefits of Perceptual Learning in Artificial Intelligence. On the one hand, Perceptual Learning is more and more studied in neurobiology and is now considered as an essential part of any living system. In fact, Perceptual Learning and Cognitive Learning are both necessary for learning and often depends on each other. On the other hand, many works in Machine Learning are concerned with "Abstraction" in order to reduce the amount of complexity related to some learning tasks. In the Abstraction framework, Perceptual Learning can be seen as a specific process that learns how to transform the data before the traditional learning task itself takes place. In this paper, we argue that biologically-inspired Perceptual Learning mechanisms could be used to build efficient low-levelAbstraction operators that deal with real world data.