C4.5: programs for machine learning
C4.5: programs for machine learning
Agents that reduce work and information overload
Communications of the ACM
Experience with a learning personal assistant
Communications of the ACM
Automated learning of decision rules for text categorization
ACM Transactions on Information Systems (TOIS)
Context-sensitive learning methods for text categorization
SIGIR '96 Proceedings of the 19th annual international ACM SIGIR conference on Research and development in information retrieval
Learning Logical Definitions from Relations
Machine Learning
ECML '93 Proceedings of the European Conference on Machine Learning
Discriminability-Based Transfer between Neural Networks
Advances in Neural Information Processing Systems 5, [NIPS Conference]
Syskill & webert: Identifying interesting web sites
AAAI'96 Proceedings of the thirteenth national conference on Artificial intelligence - Volume 1
Feature generation for sequence categorization
AAAI '98/IAAI '98 Proceedings of the fifteenth national/tenth conference on Artificial intelligence/Innovative applications of artificial intelligence
Automatic recognition of distinguishing negative indirect history language in judicial opinions
Proceedings of the tenth international conference on Information and knowledge management
Profile-Based Object Matching for Information Integration
IEEE Intelligent Systems
Constructing informative prior distributions from domain knowledge in text classification
SIGIR '06 Proceedings of the 29th annual international ACM SIGIR conference on Research and development in information retrieval
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Any system that learns how to filter documents will suffer poor performance during an initial training phase. One way of addressing this problem is to exploit filters learned by other users in a collaborative fashion. We investigate "direct transfer" of learned filters in this setting--a limiting case for any collaborative learning system. We evaluate the stability of several different learning methods under direct transfer, and conclude that symbolic learning methods that use negatively correlated features of the data perform poorly in transfer, even when they perform well in more conventional evaluation settings. This effect is robust: it holds for several learning methods, when a diverse set of users is used in training the classifier, and even when the learned classifiers can be adapted to the new user's distribution. Our experiments give rise to several concrete proposals for improving generalization performance in a collaborative setting, including a beneficial variation on a feature selection method that has been widely used in text categorization.