Aktivní učení v regresních modelech
| Thesis title in Czech: | Aktivní učení v regresních modelech |
|---|---|
| Thesis title in English: | Actve lerning in regression models |
| Key words: | regresní modely, umělé neuronové sítě, náhradní modely, časové řady, aktivní učení, semi-supervizované učení |
| English key words: | regression models, artificial neural networks, surrogate models, time series, active learning semi-supervized learning |
| Academic year of topic announcement: | 2016/2017 |
| Thesis type: | dissertation |
| Thesis language: | čeština |
| Department: | Department of Theoretical Computer Science and Mathematical Logic (32-KTIML) |
| Supervisor: | prof. RNDr. Ing. Martin Holeňa, CSc. |
| Author: | hidden - assigned and confirmed by the Study Dept. |
| Date of registration: | 20.09.2017 |
| Date of assignment: | 20.09.2017 |
| Confirmed by Study dept. on: | 03.10.2017 |
| Guidelines |
| The prospective PhD student gets familiar with the usual way of applying active learning to classification, including the possibility to combine active and semi-supervised learning. Then he concentrates on the less investigated area of applying active learning to regression, including its application to artificial neural networks, surrogate models in black-box optimization, and to time series. |
| References |
| * S. Abdullah, J.C. Allwright. An active learning approach for radial basis function neural networks. Jurnal Teknologi, 45 (2006): 77–96.
* T. Alpcan. A framework for optimization under limited information. J Glob Optim 55 (2013):6 81–706. * B. Anderson, A. Moore. Active Learning for Hidden Markov Models: Objective Functions and Algorithms, ICML 2005, 9-16. * P. Balaprakash, R.B. Gramacy, S.M. Wild. Active-Learning-Based Surrogate Models for Empirical Performance Tuning, IEEE Cluster Computing 2013. * I. Couckuyt, D. Gorissen, K. Crombecq, D. Deschrijver, T. Dhaene. The SUMO Toolbox: a Tool for Automatic Regression Modeling and Active Learning. AFRICON 2013. * D. Gorrissen. Grid-enabled Adaptive Surrogate Modeling for Computer Aided Engineering. Dissertation, 2010. * A. Krause, C. Guestrin.Nonmyopic Active Learning of Gaussian Processes: An Exploration–Exploitation Approach. ICML 2007, 449-456. * C.H. Lin, M. Mausam, D.S. Weld. Re-Active Learning: Active Learning with Relabeling, AAAI 2016, 1845-1852. * Z. Lu, X. Wu, J.C. Bongard. Active Learning through Adaptive Heterogeneous Ensembling, IEEE Transactions on Knowledge and Data Engineering 27 (2015): 368-381. * T. Schaul. Studies in Continuous Black-box Optimization. Dissertation, 2011. * B. Settles. An Analysis of Active Learning Strategies for Sequence Labeling Tasks, EMNLP 2008, 1070-1078. * B. Settles. Active Learning Literature Survey. TR Universitz of Wisconsin-Madison, 2010. * M. Sugiyama, N. Rubens. A batch ensemble approach to active learning with model selection. Neural Networks, 21 (2008): 1278–1286. * K. Tomanek, U. Hahn. Semi-Supervised Active Learning for Sequence Labeling, IJCNLP 2009, 1039-1047. * K. Yu, J. Bi, V. Tresp. Active Learning via Transductive Experimental Design. ICML 2006, 1081-1088. * L. Zhang, C. Chen, J. Bu, D. Ca, X. He, T. Huang. Active Learning based on Locally Linear Reconstruction. IEEE Transactions on Pattern Analysis and Machine Intelligence, 33 (2011): 2026-2038 * S. Zhou, Q. Chen, X. Wang. Active Semi-Supervised Learning Method with Hybrid Deep Belief Networks, Plos One, 9 (2014): e107122 |