# My papers

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The purpose of this section is to let google find the indexable publications and list. Here is a non-existent word, madgyhung, that will help me to find out when google started to index this page.

# 2010 (15)

1. , A Markov-Chain Monte Carlo Approach to Simultaneous Localization and Mapping, Proc. of the 21th International Conference on Artificial Intelligence and Statistics, JMLR: W&CP, pp. 605--612, 2010.
2. Reinforcement Learning Algorithms for MDPs, Wiley Encyclopedia of Operations Research, Wiley, 2010.
3. Some Recent Algorithmic Results about the Exploration-vs-exploitation Dilemma, 2010.

# 2009 (12)

1. , LMS-2: Towards an Algorithm that is as Cheap as LMS and Almost as Efficient as RLS, Proc. of the 48th IEEE Conference on Decision and Control (CDC-09), pp. 1181--1188, 2009.

# 2007 (7)

1. Tuning Bandit Algorithms in Stochastic Environments, ALT-07, Springer, pp. 150--165, 2007.

# 2006 (5)

1. , Learning Near-optimal Policies with Bellman-residual Minimization based Fitted Policy Iteration and a Single Sample Path, Proceedings of The Nineteenth Annual Conference on Learning Theory, COLT 2006, Machine Learning, pp. 1-- 2, 2006.

# 2005 (5)

1. , On using Likelihood-adjusted Proposals in Particle Filtering: Local Importance Sampling, 4th International Symposium on Image and Signal Processing and Analysis, pp. 1-- 2, 2005.
2. Log-optimal Currency Portfolios and Control Lyapunov Exponents, 44th IEEE Conference on Decision and Control, 2005 and 2005 European Control Conference. (CDC-ECC'05), pp. 1764--1769, 2005.

# 2003 (3)

1. Performance of Nonlinear Approximate Adaptive Controllers, Performance of Nonlinear Approximate Adaptive Controllers, Wiley, 2003.

# 2001 (5)

1. Efficient Approximate Planning in Continuous Space Markovian Decision Problems, AI Communications, 13 (3) , pp. 163--176, 2001.
2. , LS-N-IPS: an Improvement of Particle Filters by Means of Local Search, 5th IFAC Symposium on Nonlinear Control Systems (NOLCOS'01), pp. 715--719, 2001.
3. , Efficient Object Tracking in Video Sequences by means of LS-N-IPS, Proc. Second International Symposium on Image and Signal Processing and Analysis (ISAP'01), pp. 277--282, 2001.
4. Prediction of Protein Functional Domains from Sequences Using Artificial Neural Networks, Genome Research, 11 (8) , pp. 1410--1417, 2001.

# 2000 (9)

1. FlexVoice: a Parametric Approach to High-quality Speech-synthesis, IEE Seminar on State-of-the-Art in Speech Synthesis, IEE Electronics & Communications, London pp. 15/1--15/6, 2000.
2. Uncertainty, Performance, and Model Dependency in Approximate Adaptive Nonlinear Control, IEEE Transactions on Automatic Control, 45 (2) , pp. 353--358, 2000.
3. Convergent Reinforcement Learning with Value Function Interpolation, Mindmaker Ltd., (TR-2001-02) , Budapest 1121, Konkoly Th. M. u. 29-33, HUNGARY 2000.
4. Scaling of LQ Performance in Approximate Adaptive Designs, Proceedings of the International Symposium on the Mathematical Theory of Networks and Systems (MTNS 2000), 2000.

# 1999 (6)

1. The SBASE Protein Domain Sequence Library Release 6.0., Nucleic Acids Research, 27 (1) , pp. 257--259, 1999.
2. Parallel and Robust Skeletonization Built on Self-organizing Elements, Neural Networks, 12 , pp. 163--173, 1999.
3. Comparing Value-Function Estimation Algorithms in Undiscounted Problems, Mindmaker Ltd., (TR-99-02) , Budapest 1121, Konkoly Th. M. u. 29-33, Hungary 1999.

# 1998 (8)

1. Uncertainty and Performance of Adaptive Controllers for Functionally Uncertain Output Feedback Systems, Proc. of 1998 IEEE Conference on Decision and Decision, IEEE, Tampa, Florida pp. 4515--4520, 1998.
2. Non-Markovian Policies in Sequential Decision Problems, Acta Cybernetica, 13 (3) , pp. 305--318, 1998.
3. Automated Detection and Classification of Microcalcifications in Mammograms using Artificial Neural Nets, 4th International Workshop on Digital Mammography, 1998.

# 1997 (9)

1. Prediction of Protein Domain-Types by Backpropagation, Attila József'' University, Research Group on Artificial Intelligence, (TR-98-117) , Szeged, HU-6700 1997.
2. High Precision Neurocontrol of a Chaotic Bioreactor, Nonlinear Analysis, 30 (3) , pp. 1669--1676, 1997.
3. Uncertainty, Performance and Model Dependency in Approximate Adaptive Nonlinear Control, Proc. of 1997 IEEE Conference on Decision and Decision, San Diego, California pp. 3046 - 3051, 1997.
4. Learning and Exploitation do not Conflict Under Minimax Optimality, 9th European Conference of Machine Learning, Someren, M.van and Widmer, G. (Eds.), Lecture Notes in Artificial Intelligence, Springer, Berlin, pp. 242--249, 1997.

# 1996 (9)

1. Q-learning Combined with Spreading: Convergence and Results, Proceedings of ISRF-IEE International Conference: Intelligent and Cognitive Systems, Neural Networks Symposium, Tehran, Iran pp. 32--36, 1996.
2. A Generalized Reinforcement Learning Model: Convergence and Applications, ICML, pp. 310--318, 1996.
3. Generalized Markov Decision Processes: Dynamic-programming and reinforcement-learning algorithms, Brown University, Department of Computer Science, (CS-96-11) , Providence, RI 1996.
4. Synthesis of Neural Networks: the Case of Cascaded Hebbians, Research Group on Artificial Intelligence, JATE-MTA, (96-102) , Szeged 6720, Aradi vrt tere 1., HUNGARY 1996.

# 1995 (2)

1. Generalized Dynamic Concept Model as a Route to Construct Adaptive Autonomous Agents, Neural Network World, 3 , pp. 353--360, 1995.

# 1994 (7)

1. Generalization in an Autonomous Agent, Proc. of IEEE WCCI ICNN'94, IEEE Inc., Orlando, Florida pp. 1815--1817, 1994.
2. Self-organized Learning of 3 Dimensions, Proc. of ICANN'94, Marinaro, M. and Morasso, P.G. (Eds.), IEEE, Sorrento, Italy pp. 671--674, 1994.
3. , Topology Learning Solved by Extended Objects: a Neural Network Model, Neural Computation, 6 (3) , pp. 441--458, 1994.

# 1993 (4)

1. The Role of Local Connections in Competitive Neural Networks: Collective Learning and Representation of Geometry, Szeged, Hungary 1993.
2. Integration of ANN Cues, Dynamic AI Concepts and ANN Decision System into an Adaptive Self-Organizing Agent, 3rd Conf. on Artificial Intelligence, Koch, P. (Eds.), John von Neumann Society for Computer Sciences, Budapest, Hungary pp. 231--237, 1993.
3. Integration of Artificial Neural Networks and Dynamic Concepts to an adaptive and self-organizing agent, Proc. of WCNN'93, Lawrence Erlbaum Associates, Inc. Publishers, New Jersey, Portland, Oregon, USA pp. 524--527, 1993.
4. Integration of Artificial Neural Networks and Dynamic Concepts to an adaptive and self-organizing agent, Proc. of ICANN'93, Gielen, S. and Kappen, B. (Eds.), Springer-Verlag, London, Amsterdam, The Netherlands 1993.
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