Rule-Based Evolutionary Online Learning Systems: Learning Bounds, Classification, and Prediction
暂无分享,去创建一个
[1] Martin V. Butz,et al. Anticipation for learning, cognition and education , 2004, On the Horizon.
[2] Martin V. Butz,et al. Anticipatory Behavior in Adaptive Learning Systems , 2003, Lecture Notes in Computer Science.
[3] Martin V. Butz,et al. Gradient descent methods in learning classifier systems: improving XCS performance in multistep problems , 2005, IEEE Transactions on Evolutionary Computation.
[4] Pat Langley,et al. Estimating Continuous Distributions in Bayesian Classifiers , 1995, UAI.
[5] M.H. Hassoun,et al. Fundamentals of Artificial Neural Networks , 1996, Proceedings of the IEEE.
[6] Fernando G. Lobo,et al. Extended Compact Genetic Algorithm in C , 1999 .
[7] M. Colombetti,et al. An extension to the XCS classifier system for stochastic environments , 1999 .
[8] David Maxwell Chickering,et al. Learning Bayesian Networks: The Combination of Knowledge and Statistical Data , 1994, Machine Learning.
[9] Ian H. Witten,et al. Generating Accurate Rule Sets Without Global Optimization , 1998, ICML.
[10] David E. Goldberg,et al. The Design of Innovation: Lessons from and for Competent Genetic Algorithms , 2002 .
[11] Christopher Stone,et al. For Real! XCS with Continuous-Valued Inputs , 2003, Evolutionary Computation.
[12] Kalyanmoy Deb,et al. Genetic Algorithms, Noise, and the Sizing of Populations , 1992, Complex Syst..
[13] Tim Kovacs,et al. What Makes a Problem Hard for XCS? , 2000, IWLCS.
[14] K. Dejong,et al. An analysis of the behavior of a class of genetic adaptive systems , 1975 .
[15] P. Lanzi,et al. Adaptive Agents with Reinforcement Learning and Internal Memory , 2000 .
[16] G. Schwarz. Estimating the Dimension of a Model , 1978 .
[17] David E. Goldberg,et al. An Analysis of Reproduction and Crossover in a Binary-Coded Genetic Algorithm , 1987, ICGA.
[18] Gregory F. Cooper,et al. A Bayesian Method for the Induction of Probabilistic Networks from Data , 1992 .
[19] Martin V. Butz,et al. Internal Models and Anticipations in Adaptive Learning Systems , 2003, ABiALS.
[20] David E. Goldberg,et al. Implicit Niching in a Learning Classifier System: Nature's Way , 1994, Evolutionary Computation.
[21] Rocco A. Servedio,et al. Efficient algorithms in computational learning theory , 2001 .
[22] Samir W. Mahfoud. Niching methods for genetic algorithms , 1996 .
[23] David E. Goldberg,et al. Genetic Algorithms in Search Optimization and Machine Learning , 1988 .
[24] D.E. Goldberg,et al. Classifier Systems and Genetic Algorithms , 1989, Artif. Intell..
[25] Judea Pearl,et al. Probabilistic reasoning in intelligent systems - networks of plausible inference , 1991, Morgan Kaufmann series in representation and reasoning.
[26] A. Feng,et al. Neural basis of hearing in real-world situations. , 2000, Annual review of psychology.
[27] Ian H. Witten,et al. Data mining: practical machine learning tools and techniques with Java implementations , 2002, SGMD.
[28] Wray L. Buntine. Theory Refinement on Bayesian Networks , 1991, UAI.
[29] D. E. Goldberg,et al. Genetic Algorithms as a Computational Theory of Conceptual Design , 1991 .
[30] John C. Platt,et al. Fast training of support vector machines using sequential minimal optimization, advances in kernel methods , 1999 .
[31] Dana H. Ballard,et al. Learning to perceive and act by trial and error , 1991, Machine Learning.
[32] David E. Goldberg,et al. Bayesian optimization algorithm, decision graphs, and Occam's razor , 2001 .
[33] L. Valiant,et al. Leslie G. Valiant. A theory of the learnable. Communications of the ACM, , 2022 .
[34] David E. Goldberg,et al. The Race, the Hurdle, and the Sweet Spot , 1998 .
[35] Thomas G. Dietterich. What is machine learning? , 2020, Archives of Disease in Childhood.
[36] Andrew W. Moore,et al. Reinforcement Learning: A Survey , 1996, J. Artif. Intell. Res..
[37] David E. Goldberg,et al. Genetic Algorithms with Sharing for Multimodalfunction Optimization , 1987, ICGA.
[38] Leslie G. Valiant,et al. A theory of the learnable , 1984, CACM.
[39] Martin V. Butz,et al. Anticipatory Learning Classifier Systems , 2002, Genetic Algorithms and Evolutionary Computation.
[40] Michael R. James,et al. Learning and discovery of predictive state representations in dynamical systems with reset , 2004, ICML.
[41] David W. Aha,et al. Instance-Based Learning Algorithms , 1991, Machine Learning.
[42] Martin V. Butz,et al. Efiective Online Detection of Task-Independent Landmarks , 2004 .
[43] Andrew G. Barto,et al. Using relative novelty to identify useful temporal abstractions in reinforcement learning , 2004, ICML.
[44] Stewart W. Wilson. Mining Oblique Data with XCS , 2000, IWLCS.
[45] T. Kovacs. XCS Classifier System Reliably Evolves Accurate, Complete, and Minimal Representations for Boolean Functions , 1998 .
[46] Duncan Potts,et al. Incremental learning of linear model trees , 2004, ICML.
[47] Ingo Rechenberg,et al. Evolutionsstrategie : Optimierung technischer Systeme nach Prinzipien der biologischen Evolution , 1973 .
[48] Chris Drummond,et al. Accelerating Reinforcement Learning by Composing Solutions of Automatically Identified Subtasks , 2011, J. Artif. Intell. Res..
[49] Stewart W. Wilson. Classifier systems and the animat problem , 2004, Machine Learning.
[50] Ronald A. Howard,et al. Influence Diagrams , 2005, Decis. Anal..
[51] Pier Luca Lanzi,et al. A Study of the Generalization Capabilities of XCS , 1997, ICGA.
[52] Stewart W. Wilson. Classifier Systems for Continuous Payoff Environments , 2004, GECCO.
[53] Samir W. Mahfoud. Crowding and Preselection Revisited , 1992, PPSN.
[54] J. C. Johnston,et al. Attention and performance. , 2001, Annual review of psychology.
[55] Kenneth A. De Jong,et al. Learning Concept Classification Rules Using Genetic Algorithms , 1991, IJCAI.
[56] Catherine Blake,et al. UCI Repository of machine learning databases , 1998 .
[57] Ester Bernadó-Mansilla,et al. Accuracy-Based Learning Classifier Systems: Models, Analysis and Applications to Classification Tasks , 2003, Evolutionary Computation.
[58] Andreas Rauber,et al. The growing hierarchical self-organizing map , 2000, Proceedings of the IEEE-INNS-ENNS International Joint Conference on Neural Networks. IJCNN 2000. Neural Computing: New Challenges and Perspectives for the New Millennium.
[59] Thomas G. Dietterich. Machine-Learning Research Four Current Directions , 1997 .
[60] Wolfgang Stolzmann,et al. Anticipatory Classifier Systems: An introduction , 2001 .
[61] F. W. Irwin. Purposive Behavior in Animals and Men , 1932, The Psychological Clinic.
[62] L. Baird. Reinforcement Learning Through Gradient Descent , 1999 .
[63] Dirk Thierens,et al. Toward a Better Understanding of Mixing in Genetic Algorithms , 1993 .
[64] Stewart W. Wilson. Classifier Fitness Based on Accuracy , 1995, Evolutionary Computation.
[65] Robert C. Holte,et al. Very Simple Classification Rules Perform Well on Most Commonly Used Datasets , 1993, Machine Learning.
[66] Martin V. Butz,et al. An algorithmic description of XCS , 2000, Soft Comput..
[67] Martin V. Butz,et al. Toward a theory of generalization and learning in XCS , 2004, IEEE Transactions on Evolutionary Computation.
[68] Stewart W. Wilson. The animat path to AI , 1991 .
[69] Kalyanmoy Deb,et al. A Comparative Analysis of Selection Schemes Used in Genetic Algorithms , 1990, FOGA.
[70] Olivier Sigaud,et al. Adding a generalization mechanism to YACS , 2001 .
[71] A. Martin V. Butz,et al. The anticipatory classifier system and genetic generalization , 2002, Natural Computing.
[72] Melanie Mitchell,et al. The royal road for genetic algorithms: Fitness landscapes and GA performance , 1991 .
[73] Stewart W. WilsonPrediction. Function Approximation with a Classi er System , 2001 .
[74] Richard S. Sutton,et al. Predictive Representations of State , 2001, NIPS.
[75] Martin V. Butz,et al. Gradient-Based Learning Updates Improve XCS Performance in Multistep Problems , 2004, GECCO.
[76] Herbert A. Simon,et al. The Sciences of the Artificial , 1970 .
[77] James E. Baker,et al. Adaptive Selection Methods for Genetic Algorithms , 1985, International Conference on Genetic Algorithms.
[78] Arthur Gelb,et al. Applied Optimal Estimation , 1974 .
[79] Martin V. Butz,et al. Data Mining in Learning Classifier Systems: Comparing XCS with GAssist , 2005, IWLCS.
[80] John H. Holland,et al. Adaptation in Natural and Artificial Systems: An Introductory Analysis with Applications to Biology, Control, and Artificial Intelligence , 1992 .
[81] John H. Holland,et al. Cognitive systems based on adaptive algorithms , 1977, SGAR.
[82] G. Harik. Linkage Learning via Probabilistic Modeling in the ECGA , 1999 .
[83] Leonard Kleinrock,et al. Queueing Systems: Volume I-Theory , 1975 .
[84] Joachim Hoffmann,et al. Anticipatory Behavioral Control , 2003, ABiALS.
[85] David E. Goldberg,et al. Probability Matching, the Magnitude of Reinforcement, and Classifier System Bidding , 1990, Machine Learning.
[86] Luca Lanzi Pier,et al. Extending the Representation of Classifier Conditions Part II: From Messy Coding to S-Expressions , 1999 .
[87] Lashon B. Booker,et al. Intelligent Behavior as an Adaptation to the Task Environment , 1982 .
[88] MSc PhD Tim Kovacs BA. Strength or Accuracy: Credit Assignment in Learning Classifier Systems , 2004, Distinguished Dissertations.
[89] Richard S. Sutton,et al. Reinforcement learning architectures for animats , 1991 .
[90] Pier Luca Lanzi,et al. An Analysis of Generalization in the XCS Classifier System , 1999, Evolutionary Computation.
[91] G. Aschersleben,et al. The Theory of Event Coding (TEC): a framework for perception and action planning. , 2001, The Behavioral and brain sciences.
[92] David E. Goldberg,et al. A Survey of Optimization by Building and Using Probabilistic Models , 2002, Comput. Optim. Appl..
[93] Morgan B Kaufmann,et al. Finite Markov Chain Analysis of Genetic Algorithms with Niching , 1993 .
[94] D. Goldberg,et al. Bounding Learning Time in XCS , 2004, GECCO.
[95] Gilles Venturini,et al. Adaptation in dynamic environments through a minimal probability of exploration , 1994 .
[96] Stewart W. Wilson. Knowledge Growth in an Artificial Animal , 1985, ICGA.
[97] Nello Cristianini,et al. Kernel Methods for Pattern Analysis , 2006 .
[98] Martin V. Butz,et al. Bounding the Population Size in XCS to Ensure Reproductive Opportunities , 2003, GECCO.
[99] D. Goldberg,et al. A practical schema theorem for genetic algorithm design and tuning , 2001 .
[100] Xavier Llorà,et al. Bounding the Effect of Noise in Multiobjective Learning Classifier Systems , 2003, Evolutionary Computation.
[101] David E. Goldberg,et al. A Critical Review of Classifier Systems , 1989, ICGA.
[102] Tim Kovacs,et al. Strength or Accuracy? Fitness Calculation in Learning Classifier Systems , 1999, Learning Classifier Systems.
[103] D. Goldberg,et al. Domino convergence, drift, and the temporal-salience structure of problems , 1998, 1998 IEEE International Conference on Evolutionary Computation Proceedings. IEEE World Congress on Computational Intelligence (Cat. No.98TH8360).
[104] Stewart W. Wilson. ZCS: A Zeroth Level Classifier System , 1994, Evolutionary Computation.
[105] Sean R Eddy,et al. What is dynamic programming? , 2004, Nature Biotechnology.
[106] M. Pelikán,et al. Analyzing the evolutionary pressures in XCS , 2001 .
[107] Leemon C. Baird,et al. Residual Algorithms: Reinforcement Learning with Function Approximation , 1995, ICML.
[108] David E. Goldberg,et al. Accuracy, Parsimony, and Generality in Evolutionary Learning Systems via Multiobjective Selection , 2002, IWLCS.
[109] Sridhar Mahadevan,et al. Recent Advances in Hierarchical Reinforcement Learning , 2003, Discret. Event Dyn. Syst..
[110] David E. Goldberg,et al. Bayesian optimization algorithm: from single level to hierarchy , 2002 .
[111] T. Kovacs. Deletion Schemes for Classi er Systems , 1999 .
[112] D. Goldberg,et al. Minimal Achievable Error in the LED problem , 2002 .
[113] Lashon B. Booker,et al. Recombination Distributions for Genetic Algorithms , 1992, FOGA.
[114] Martin V. Butz,et al. How XCS evolves accurate classifiers , 2001 .
[115] Olivier Sigaud,et al. YACS: Combining Dynamic Programming with Generalization in Classifier Systems , 2000, IWLCS.
[116] Stewart W. Wilson. The Genetic Algorithm and Simulated Evolution , 1987, ALIFE.
[117] Pedro Larrañaga,et al. A Review on Estimation of Distribution Algorithms , 2002, Estimation of Distribution Algorithms.
[118] Richard S. Sutton,et al. Integrated Architectures for Learning, Planning, and Reacting Based on Approximating Dynamic Programming , 1990, ML.
[119] Luc De Raedt,et al. Bellman goes relational , 2004, ICML.
[120] Stewart W. Wilson. Generalization in the XCS Classifier System , 1998 .
[121] Olivier Sigaud,et al. Designing Efficient Exploration with MACS: Modules and Function Approximation , 2003, GECCO.
[122] Thomas G. Dietterich. Hierarchical Reinforcement Learning with the MAXQ Value Function Decomposition , 1999, J. Artif. Intell. Res..
[123] A. Gray,et al. I. THE ORIGIN OF SPECIES BY MEANS OF NATURAL SELECTION , 1963 .
[124] Doina Precup,et al. Between MDPs and Semi-MDPs: A Framework for Temporal Abstraction in Reinforcement Learning , 1999, Artif. Intell..
[125] Stewart W. Wilson,et al. Toward Optimal Classifier System Performance in Non-Markov Environments , 2000, Evolutionary Computation.
[126] Pier Luca Lanzi. Learning classifier systems from a reinforcement learning perspective , 2002, Soft Comput..
[127] Tim Kovacs,et al. Towards a Theory of Strong Overgeneral Classifiers , 2000, FOGA.
[128] Peter Stone,et al. Learning Predictive State Representations , 2003, ICML.
[129] Ronald A. Howard,et al. Readings on the Principles and Applications of Decision Analysis , 1989 .
[130] Stewart W. Wilson. Get Real! XCS with Continuous-Valued Inputs , 1999, Learning Classifier Systems.
[131] Dirk Thierens,et al. Mixing in Genetic Algorithms , 1993, ICGA.
[132] David Maxwell Chickering,et al. A Bayesian Approach to Learning Bayesian Networks with Local Structure , 1997, UAI.
[133] H. Crichton-Miller. Adaptation , 1926 .
[134] S. Grossberg,et al. Adaptive pattern classification and universal recoding: I. Parallel development and coding of neural feature detectors , 1976, Biological Cybernetics.
[135] Nir Friedman,et al. Learning Bayesian Networks with Local Structure , 1996, UAI.
[136] H. Pashler. The Psychology of Attention , 1997 .
[137] Martin V. Butz,et al. Strong, Stable, and Reliable Fitness Pressure in XCS due to Tournament Selection , 2005, Genetic Programming and Evolvable Machines.
[138] J. Hoffmann,et al. Anticipated Action Effects Affect the Selection, Initiation, and Execution of Actions , 2004, The Quarterly journal of experimental psychology. A, Human experimental psychology.
[139] Max Henrion,et al. Propagating uncertainty in bayesian networks by probabilistic logic sampling , 1986, UAI.
[140] Stephen Grossberg,et al. Adaptive pattern classification and universal recoding: II. Feedback, expectation, olfaction, illusions , 1976, Biological Cybernetics.
[141] Chris Watkins,et al. Learning from delayed rewards , 1989 .
[142] Larry Bull. Investigating fitness sharing in a simple payoff-based learning classifier system , 2003 .
[143] D. Goldberg,et al. BOA: the Bayesian optimization algorithm , 1999 .
[144] T. Kovacs. Deletion schemes for classifier systems , 1999 .
[145] Richard S. Sutton,et al. Reinforcement Learning: An Introduction , 1998, IEEE Trans. Neural Networks.
[146] er SystemsTim KovacsOctober. Evolving Optimal Populations with XCS Classi , 1996 .
[147] LanziPier Luca. An analysis of generalization in the xcs classifier system , 1999 .
[148] C. Atkeson,et al. Prioritized Sweeping: Reinforcement Learning with Less Data and Less Time , 1993, Machine Learning.
[149] Herbert Jaeger,et al. Observable Operator Models for Discrete Stochastic Time Series , 2000, Neural Computation.
[150] Martin V. Butz,et al. Analysis and Improvement of Fitness Exploitation in XCS: Bounding Models, Tournament Selection, and Bilateral Accuracy , 2003, Evolutionary Computation.
[151] Stewart W. Wilson. Function approximation with a classifier system , 2001 .