Explanation-based learning for mobile-robot perception
Symbolic visual learning
Explanation-Based Neural Network Learning for Robot Control
Advances in Neural Information Processing Systems 5, [NIPS Conference]
Rule-Injection Hints as a Means of Improving Network Performance and Learning Time
Proceedings of the EURASIP Workshop 1990 on Neural Networks
Learning One More Thing
Tracking Context Changes through Meta-Learning
Machine Learning - Special issue on multistrategy learning
A perspective view and survey of meta-learning
Artificial Intelligence Review
The Pragmatic Roots of Context
CONTEXT '99 Proceedings of the Second International and Interdisciplinary Conference on Modeling and Using Context
Introduction to the Special Issue on Meta-Learning
Machine Learning
Boosting for transfer learning
Proceedings of the 24th international conference on Machine learning
Mindful: A framework for Meta-INDuctive neuro-FUzzy Learning
Information Sciences: an International Journal
Learning Similarity with Operator-valued Large-margin Classifiers
The Journal of Machine Learning Research
Spectral domain-transfer learning
Proceedings of the 14th ACM SIGKDD international conference on Knowledge discovery and data mining
Bridged Refinement for Transfer Learning
PKDD 2007 Proceedings of the 11th European conference on Principles and Practice of Knowledge Discovery in Databases
Transferred Dimensionality Reduction
ECML PKDD '08 Proceedings of the European conference on Machine Learning and Knowledge Discovery in Databases - Part II
Meta-learning optimal parameter values in non-stationary environments
Knowledge-Based Systems
Cross-Domain Knowledge Transfer Using Semi-supervised Classification
AI '08 Proceedings of the 21st Australasian Joint Conference on Artificial Intelligence: Advances in Artificial Intelligence
Transferring naive bayes classifiers for text classification
AAAI'07 Proceedings of the 22nd national conference on Artificial intelligence - Volume 1
Transferring localization models over time
AAAI'08 Proceedings of the 23rd national conference on Artificial intelligence - Volume 3
A model of inductive bias learning
Journal of Artificial Intelligence Research
Transfer Learning beyond Text Classification
ACML '09 Proceedings of the 1st Asian Conference on Machine Learning: Advances in Machine Learning
Active reranking for web image search
IEEE Transactions on Image Processing
Knowledge transferring via implicit link analysis
DASFAA'08 Proceedings of the 13th international conference on Database systems for advanced applications
Using learning algorithm behavior to chart task space: The DICES distance
Intelligent Data Analysis
Investigation in transfer learning: better way to apply transfer learning between agents
MLDM'11 Proceedings of the 7th international conference on Machine learning and data mining in pattern recognition
Using advice to transfer knowledge acquired in one reinforcement learning task to another
ECML'05 Proceedings of the 16th European conference on Machine Learning
HyParSVM: a new hybrid parallel software for support vector machine learning on SMP clusters
Euro-Par'06 Proceedings of the 12th international conference on Parallel Processing
ACCV'09 Proceedings of the 9th Asian conference on Computer Vision - Volume Part III
Paralinguistics in speech and language-State-of-the-art and the challenge
Computer Speech and Language
| Hi-index | 0.00 |
Most research on machine learning has focused on scenarios in which a learner faces a single isolated learning task. The lifelong learning framework assume, that the learner encounters a multitude of related learning tasks over Us lifetime providing the opportunity for the transfer of knowledge among these. 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 of invariances are learned and used to bias subsequent learning.