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Reinforcement learning and its applications to control and multi-agent systems is where Dr. Jing Peng's doctoral studies originated. In general terms, reinforcement learning is concerned with computational approaches to learning from reward. Here he considers how a learning agent can learn as quickly as possible from limited interaction with other agents and its environment.
Image retrieval, in particular content-based retrieval where he considers indexing schemes that allow flexible retrieval metrics to be created on the fly so that very large databases can be accessed efficiently and accurately.
Classification and data mining, is where he is particularly interested in adaptive metric nearest-neighbor techniques and compact subspace representation for building robust classifiers from limited training data.
Research interests
Classification and Data Mining
Scholarly Interests
Adaptive metric nearest-neighbor techniques and compact subspace representation for building robust classifiers from limited training data.
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Computational Intelligence Based Power Systems Operation
Singh, B. N. B. N., Peng, J. & Simina, M. M.
1/07/04 → 30/06/09
Project: Research
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SGER: Flexible Index Structure for Relevance Feedback Content-Based Retrieval in Large Image Databases
Peng, J. & Heisterkamp, D. R.
1/11/01 → 30/11/02
Project: Research
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A Report on the Euphemisms Detection Shared Task
Lee, P., Feldman, A. & Peng, J., 2022, FLP 2022 - 3rd Workshop on Figurative Language Processing, Proceedings of the Workshop. Association for Computational Linguistics (ACL), p. 184-190 7 p. (FLP 2022 - 3rd Workshop on Figurative Language Processing, Proceedings of the Workshop).Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review
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CATs are Fuzzy PETs: A Corpus and Analysis of Potentially Euphemistic Terms
Gavidia, M., Lee, P., Feldman, A. & Peng, J., 2022, 2022 Language Resources and Evaluation Conference, LREC 2022. Calzolari, N., Bechet, F., Blache, P., Choukri, K., Cieri, C., Declerck, T., Goggi, S., Isahara, H., Maegaard, B., Mariani, J., Mazo, H., Odijk, J. & Piperidis, S. (eds.). European Language Resources Association (ELRA), p. 2658-2671 14 p. (2022 Language Resources and Evaluation Conference, LREC 2022).Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review
4 Scopus citations -
Searching for PETs: Using Distributional and Sentiment-Based Methods to Find Potentially Euphemistic Terms
Lee, P., Gavidia, M., Feldman, A. & Peng, J., 2022, UnImplicit 2022 - 2nd Workshop on Understanding Implicit and Underspecified Language, Proceedings of the Workshop. Pyatkin, V., Fried, D. & Anthonio, T. (eds.). Association for Computational Linguistics (ACL), p. 22-32 11 p. (UnImplicit 2022 - 2nd Workshop on Understanding Implicit and Underspecified Language, Proceedings of the Workshop).Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review
7 Scopus citations -
Learning Latent Variable Models with Discriminant Regularization
Peng, J. & Aved, A. J., 2021, Agents and Artificial Intelligence - 12th International Conference, ICAART 2020, Revised Selected Papers. Rocha, A. P., Steels, L. & van den Herik, J. (eds.). Springer Science and Business Media Deutschland GmbH, p. 378-398 21 p. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); vol. 12613 LNAI).Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review
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Learning Latent Variable Models with Regularization
Peng, J., 2021, International Conference on Electrical, Computer, and Energy Technologies, ICECET 2021. Institute of Electrical and Electronics Engineers Inc., (International Conference on Electrical, Computer, and Energy Technologies, ICECET 2021).Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review