Research interest

Related Work:

Conferences and Workshops

NIPS*99 Workshop on Using unlabeled data for supervised learning
NIPS 2000 Workshop: Unlabeled Data Supervised Learning Competition
Unlabeled Data 2001: It's time to put-up or shut-up!
COLT - Annual Conference on Computational Learning Theory
ICML - International Conference on Machine Learning
NIPS - Annual Conference on Neural Information Processing Systems
EUROCOLT - European Conference on Computational Learning Theory
ALT - Annual Conference on Algorithmic Learning Theory
AAAI - The National Conference on Artificial Intelligence
UAI - Uncertainty in Artificial Intelligence
KDD - Knowledge Discovery in Data and Data Mining



1. Related Publications

   Semi-Supervised Learning
   

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   Matthias Seeger. Learning with Labeled and Unlabeled Data. Technical Report,Edinburgh University
   
   A. Blum and S. Chawla. Learning from Labeled and Unlabeled Data using Graph Mincuts. ICML, 2001.
   
   A. Blum and T. Mitchell. Combining Labeled and Unlabeled Data with Co-Training. COLT, pages 92--100, 1998.
   
   Kamal Nigam and Rayid Ghani. Analyzing the Effectiveness and Applicability of Co-training. The Ninth International Conference on Information and Knowledge Management (CIKM-2000). 2000.
   
   Kamal Nigam, Andrew McCallum, Sebastian Thrun and Tom Mitchell. Text Classification from Labeled and Unlabeled Documents using EM. Machine Learning, 39(2/3). pp. 103-134. 2000.
   
   Kamal Nigam and Rayid Ghani. Understanding the Behavior of Co-training. In KDD-2000 Workshop on Text Mining. 2000.
   
   Yuri Ivanov,Bruce Blumberg,Alex Pentland. Expectation Maximization for Weakly Labeled Data ICML 2001.
   
   S. Baluja. Probabilistic Modeling for Face Orientation Discrimination: Learning from Labeled and Unlabeled Data. Neural Information Procesing systems (NIPS '98).
   
   T. Mitchell. The Role of Unlabeled Data in Supervised Learning. Proceedings of the Sixth International Colloquium on Cognitive Science, San Sebastian, Spain, 1999 (invited paper).
   
   Kristin P. Bennett,A. Demiriz. 1999. Semi-Supervised Support Vector Machines. Advances in Neural Information Processing Systems, 12.
   
   G. Fung and O. L. Mangasarian. Semi-Supervised Support Vector Machines for Unlabeled Data Classification. Data Mining Institute Technical Report 99-05, October 1999.
   
   Ratsaby, J., & Venkatesh, S. S. Learning from a mixture of labeled and unlabeled examples with parametric side information. In Proceedings of the Eighth Annual Conference on Computational Learning Theory, pp. 412-417.
   
   D. Miller and Uyar. A Mixture of Experts Classifier with Learning Based on Both Labelled and Unlabelled Data. Advances in Neural Information Processing (NIPS-9), pp. 571-577
   
   T. Zhang and F. Oles. A Probability Analysis on the Value of Unlabeled Data for Classification Problems. International Conference on Machine Learning; June, 2000.

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   Active Learning
   

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   D. Cohn, Z. Ghahramani, and M. I. Jordan. Active learning with statistical models. Journal of Artificial Intelligence Research, 4, 129-145, 1996.
   
   David D. Lewis, William A. Gale. A Sequential Algorithm for Training Text Classifiers. Proceedings of SIGIR-94, 17th ACM International Conference on Research and Development in Information Retrieval(pp. 3-12)
   David D. Lewis, Jason Catlett. Heterogeneous Uncertainty Sampling for Supervised Learning. Proc. 11th International Conference on Machine Learning.
   H. S. Seung, M. Opper, H. Sompolinsky. Query by Committee Proc. 5th Annu. Workshop on Comput. Learning Theory (pp. 287-294)
   Yoav Freund, H. Sebastian Seung, Eli Shamir, Naftali Tishby. Selective sampling using the Query by Committee algorithm Machine Learning, 28, 133-168.
   Nick Roy and Andrew McCallum. Toward Optimal Active Learning through Sampling Estimation of Error Reduction. ICML-2001 (pp. 441-448)
   Greg Schohn, David Cohn Less is More: Active Learning with Support Vector Machines Proc. 17th International Conf. on Machine Learning, 2000
   Simon Tong, Daphne Koller. Support Vector Machine Active Learning with Applications to Text Classification. To appear Machine Learning Journal 2001.
   
   

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   Transductive Learning
   

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   Joachims,Thorsten Joachims. Transductive Inference for Text Classification using Support Vector Machines. International Conference on Machine Learning. (ICML), 1999.
   
   

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   Text Categorization
   

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   Andrew McCallum, Kamal Nigam. Employing EM and Pool-Based Active Learning for Text Classification Proc. 15th International Conf. on Machine Learning, 1998
   
   Kamal Nigam, Andrew McCallum, Sebastian Thrun, Tom Mitchell. Learning to Classify Text from Labeled and Unlabeled Documents Proceedings of AAAI-98, 15th Conference of the American Association for Artificial Intelligence
   
   Andrew McCallum, Kamal Nigam. 1999. Text Classification by Bootstrapping with Keywords, EM and Shrinkage.
   
   Rosie Jones, Andrew McCallum, Kamal Nigam, Ellen Riloff. Bootstrapping for Text Learning Tasks. IJCAI-99 Workshop on Text Mining: Foundations, Techniques and Applications, 1999
   
   Kamal Nigam Andrew McCallum Sebastian Thrun Tom Mitchell. 1998. Using EM to Classify Text from Labeled and Unlabeled Documents
   
   kamal Nigam, John Lafferty, Andrew McCallum. 1999. Using Maximum Entropy for Text Classification

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Machine Learning Resources

Machine Learning Tools

Artificial Intelligence

Networking Tools

Recent Publications

• (With S. Goldman), Enhancing Supervised Learning with Unlabeled Data, ICML, 2000
  
  

• (With D. Wilkins, R. P. Cook), "Neural Network Control for A Fire-Fighting Robot," Software -- Concepts and Tools 19 3, (1999) 146-152.

Master Thesis