Algorithms for clustering data
Algorithms for clustering data
Learnability and the Vapnik-Chervonenkis dimension
Journal of the ACM (JACM)
Automated learning of decision rules for text categorization
ACM Transactions on Information Systems (TOIS)
Tracking Drifting Concepts By Minimizing Disagreements
Machine Learning - Special issue on computational learning theory
Handbook of logic in artificial intelligence and logic programming (Vol. 4): epistemic and temporal reasoning
Learning in the presence of concept drift and hidden contexts
Machine Learning
Learning changing concepts by exploiting the structure of change
COLT '96 Proceedings of the ninth annual conference on Computational learning theory
Incremental relevance feedback for information filtering
SIGIR '96 Proceedings of the 19th annual international ACM SIGIR conference on Research and development in information retrieval
Tracking Context Changes through Meta-Learning
Machine Learning - Special issue on multistrategy learning
An adaptive Web page recommendation service
AGENTS '97 Proceedings of the first international conference on Autonomous agents
Evolving a multi-agent information filtering solution in Amalthaea
AGENTS '97 Proceedings of the first international conference on Autonomous agents
Combining labeled and unlabeled data with co-training
COLT' 98 Proceedings of the eleventh annual conference on Computational learning theory
WebMate: a personal agent for browsing and searching
AGENTS '98 Proceedings of the second international conference on Autonomous agents
Improving automatic query expansion
Proceedings of the 21st annual international ACM SIGIR conference on Research and development in information retrieval
Machine Learning - Special issue on context sensitivity and concept drift
A personal news agent that talks, learns and explains
Proceedings of the third annual conference on Autonomous Agents
An adaptive algorithm for learning changes in user interests
Proceedings of the eighth international conference on Information and knowledge management
Real life, real users, and real needs: a study and analysis of user queries on the web
Information Processing and Management: an International Journal
Learning user interest dynamics with a three-descriptor representation
Journal of the American Society for Information Science and Technology
Machine Learning
Managing Gigabytes: Compressing and Indexing Documents and Images
Managing Gigabytes: Compressing and Indexing Documents and Images
Introduction to Modern Information Retrieval
Introduction to Modern Information Retrieval
PVA: A Self-Adaptive Personal View Agent
Journal of Intelligent Information Systems
Learning Approaches for Detecting and Tracking News Events
IEEE Intelligent Systems
Detecting Concept Drift with Support Vector Machines
ICML '00 Proceedings of the Seventeenth International Conference on Machine Learning
Improving Short-Text Classification using Unlabeled Data for Classification Problems
ICML '00 Proceedings of the Seventeenth International Conference on Machine Learning
Learning from Labeled and Unlabeled Data using Graph Mincuts
ICML '01 Proceedings of the Eighteenth International Conference on Machine Learning
An Incremental Approach to Building a Cluster Hierarchy
ICDM '02 Proceedings of the 2002 IEEE International Conference on Data Mining
Non-stationary data sequence classification using online class priors estimation
Pattern Recognition
Information Processing and Management: an International Journal
Intrusion Prevention in Information Systems: Reactive and Proactive Responses
Journal of Management Information Systems
Maintaining Diagnostic Knowledge-Based Systems: A Control-Theoretic Approach
Management Science
Learning dynamic information needs: A collaborative topic variation inspection approach
Journal of the American Society for Information Science and Technology
A fuzzy combined learning approach to content-based image retrieval
ICME'09 Proceedings of the 2009 IEEE international conference on Multimedia and Expo
Collaborative content and user-based web ontology learning system
FUZZ-IEEE'09 Proceedings of the 18th international conference on Fuzzy Systems
Transfer estimation of evolving class priors in data stream classification
Pattern Recognition
Active learning with evolving streaming data
ECML PKDD'11 Proceedings of the 2011 European conference on Machine learning and knowledge discovery in databases - Volume Part III
Exploring classification concept drift on a large news text corpus
CICLing'12 Proceedings of the 13th international conference on Computational Linguistics and Intelligent Text Processing - Volume Part I
Semi-supervised ensemble learning of data streams in the presence of concept drift
HAIS'12 Proceedings of the 7th international conference on Hybrid Artificial Intelligent Systems - Volume Part II
Image retrieval using transaction-based and SVM-based learning in relevance feedback sessions
ICIAR'07 Proceedings of the 4th international conference on Image Analysis and Recognition
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Keeping track of changing interests is a natural phenomenon as well as an interesting tracking problem because interests can emerge and diminish at different time frames. Being able to do so with a few feedback examples poses an even more important and challenging problem because existing concept drift learning algorithms that handle the task typically suffer from it. This paper presents a new computational Framework for Extending Incomplete Labeled Data Stream (FEILDS), which extends the capability of existing algorithms for learning concept drift from a few labeled data. The system transforms the original input stream into a new stream that can be conveniently tracked by the existing learning algorithms. The experiment results reveal that FEILDS can significantly improve the performances of a Multiple Three-Descriptor Representation (MTDR) algorithm, Rocchio algorithm, and window-based concept drift learning algorithms when learning from a sparsely labeled data stream with respect to their performances without using FEILDS.