Rapid Concept Learning for Mobile Robots
Machine Learning - Special issue on learning in autonomous robots
Rapid Concept Learning for Mobile Robots
Autonomous Robots
Benefitting from the variables that variable selection discards
The Journal of Machine Learning Research
IJCAI'95 Proceedings of the 14th international joint conference on Artificial intelligence - Volume 2
A multitask learning model for online pattern recognition
IEEE Transactions on Neural Networks
Evolving Static Representations for Task Transfer
The Journal of Machine Learning Research
Using previous models to bias structural learning in the hierarchical boa
Evolutionary Computation
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Most research on machine learning has focused on scenarios in which a learner faces a single, isolated learning task. The lifelong learning framework assumes instead that the learner encounters a multitude of related learning tasks over its lifetime, providing the opportunity for the transfer of knowledge. This paper studies lifelong learning in the context of binary classification. It presents the invariance approach, in which knowledge is transferred via a learned model of the invariances of the domain. Results on learning to recognize objects from color images demonstrate superior generalization capabilities if invariances are learned and used to bias subsequent learning.