暂无分享,去创建一个
[1] Rich Hickey,et al. The Clojure programming language , 2008, DLS '08.
[2] Ronald E. Parr,et al. Non-Myopic Multi-Aspect Sensing with Partially Observable Markov Decision Processes , 2005 .
[3] J. Gittins,et al. A dynamic allocation index for the discounted multiarmed bandit problem , 1979 .
[4] Andreas Krause,et al. Efficient Planning of Informative Paths for Multiple Robots , 2006, IJCAI.
[5] L. van der Gaag,et al. Selective evidence gathering for diagnostic belief networks , 1993 .
[6] Edward J. Sondik,et al. The Optimal Control of Partially Observable Markov Processes over a Finite Horizon , 1973, Oper. Res..
[7] Eric Horvitz,et al. An Approximate Nonmyopic Computation for Value of Information , 1993, IEEE Trans. Pattern Anal. Mach. Intell..
[8] Salil P. Vadhan,et al. Computational Complexity , 2005, Encyclopedia of Cryptography and Security.
[9] John Langford,et al. Agnostic active learning , 2006, J. Comput. Syst. Sci..
[10] Vijay S. Mookerjee,et al. Sequential Decision Models for Expert System Optimization , 1997, IEEE Trans. Knowl. Data Eng..
[11] Andreas Krause,et al. Efficient Sensor Placement Optimization for Securing Large Water Distribution Networks , 2008 .
[12] Lluís A. Belanche Muñoz,et al. Feature selection algorithms: a survey and experimental evaluation , 2002, 2002 IEEE International Conference on Data Mining, 2002. Proceedings..
[13] Miodrag Potkonjak,et al. Sleeping Coordination for Comprehensive Sensing Using Isotonic Regression and Domatic Partitions , 2006, Proceedings IEEE INFOCOM 2006. 25TH IEEE International Conference on Computer Communications.
[14] Andreas Krause,et al. Near-optimal Nonmyopic Value of Information in Graphical Models , 2005, UAI.
[15] Bruce Edmonds,et al. Meta-Genetic Programming: Co-evolving the Operators of Variation , 2001 .
[16] Peter G. Harrison,et al. Functional Programming , 1988 .
[17] Stephen P. Boyd,et al. Convex Optimization , 2004, Algorithms and Theory of Computation Handbook.
[18] Michael L. Littman,et al. The Computational Complexity of Probabilistic Planning , 1998, J. Artif. Intell. Res..
[19] David A. Cohn,et al. Active Learning with Statistical Models , 1996, NIPS.
[20] Wei Hong,et al. Model-Driven Data Acquisition in Sensor Networks , 2004, VLDB.
[21] R. L. Keeney,et al. Decisions with Multiple Objectives: Preferences and Value Trade-Offs , 1977, IEEE Transactions on Systems, Man, and Cybernetics.
[22] Yoav Shoham,et al. Optimal Testing of Structured Knowledge , 2008, AAAI.
[23] Maarten Keijzer,et al. The Push3 execution stack and the evolution of control , 2005, GECCO '05.
[24] Lee Spector,et al. Towards Practical Autoconstructive Evolution: Self-Evolution of Problem-Solving Genetic Programming Systems , 2011 .
[25] John H. Holland,et al. Adaptation in Natural and Artificial Systems: An Introductory Analysis with Applications to Biology, Control, and Artificial Intelligence , 1992 .
[26] A. Darwiche,et al. Complexity Results and Approximation Strategies for MAP Explanations , 2011, J. Artif. Intell. Res..
[27] Eric Horvitz,et al. Selective Supervision: Guiding Supervised Learning with Decision-Theoretic Active Learning , 2007, IJCAI.
[28] Samir Khuller,et al. Energy Efficient Monitoring in Sensor Networks , 2008, LATIN.
[29] D. Lindley. On a Measure of the Information Provided by an Experiment , 1956 .
[30] Joelle Pineau,et al. Anytime Point-Based Approximations for Large POMDPs , 2006, J. Artif. Intell. Res..
[31] Peter D. Turney. Cost-Sensitive Classification: Empirical Evaluation of a Hybrid Genetic Decision Tree Induction Algorithm , 1994, J. Artif. Intell. Res..
[32] Ronald A. Howard,et al. Information Value Theory , 1966, IEEE Trans. Syst. Sci. Cybern..
[33] Maurice Queyranne,et al. An Exact Algorithm for Maximum Entropy Sampling , 1995, Oper. Res..
[34] Ashish Goel,et al. Set k-cover algorithms for energy efficient monitoring in wireless sensor networks , 2003, Third International Symposium on Information Processing in Sensor Networks, 2004. IPSN 2004.
[35] Daphne Koller,et al. Active Learning for Parameter Estimation in Bayesian Networks , 2000, NIPS.
[36] L. Spector. Adaptive populations of endogenously diversifying Pushpop organisms are reliably diverse , 2002 .
[37] Rick L. Riolo,et al. Genetic Programming Theory and Practice VIII , 2010 .
[38] L. Baum,et al. Statistical Inference for Probabilistic Functions of Finite State Markov Chains , 1966 .
[39] Ronald A. Howard,et al. Readings on the Principles and Applications of Decision Analysis , 1989 .
[40] Lawrence Carin,et al. Nonmyopic Multiaspect Sensing With Partially Observable Markov Decision Processes , 2007, IEEE Transactions on Signal Processing.
[41] Stefan Wrobel,et al. Active Learning of Partially Hidden Markov Models , 2001 .
[42] Lise Getoor,et al. VOILA: Efficient Feature-value Acquisition for Classification , 2007, AAAI.
[43] Sean R. Eddy,et al. Biological Sequence Analysis: Probabilistic Models of Proteins and Nucleic Acids , 1998 .
[44] Nicholas Roy,et al. Global A-Optimal Robot Exploration in SLAM , 2005, Proceedings of the 2005 IEEE International Conference on Robotics and Automation.
[45] R. Bellman. A Markovian Decision Process , 1957 .
[46] Finn Verner Jensen,et al. Myopic Value of Information in Influence Diagrams , 1997, UAI.
[47] Jordan B. Pollack,et al. Co-Evolving Intertwined Spirals , 1996, Evolutionary Programming.
[48] Feng Zhao,et al. Information-Driven Dynamic Sensor Collaboration for Tracking Applications , 2002 .
[49] Lee Spector,et al. Autoconstructive Evolution: Push, PushGP, and Pushpop , 2001 .
[50] Solomon Eyal Shimony,et al. Efficient Deterministic Approximation Algorithms for Non-myopic Value of Information in Graphical Models , 2006, 2006 IEEE International Conference on Systems, Man and Cybernetics.
[51] Valentina Bayer-Zubek. Learning diagnostic policies from examples by systematic search , 2004, UAI 2004.
[52] Leslie Ann Goldberg,et al. COUNTING UNLABELLED SUBTREES OF A TREE IS #P-COMPLETE , 2000 .
[53] Thomas M. Cover,et al. Elements of Information Theory , 2005 .
[54] E. J. Sondik,et al. The Optimal Control of Partially Observable Markov Decision Processes. , 1971 .
[55] Pedro M. Domingos,et al. On the Optimality of the Simple Bayesian Classifier under Zero-One Loss , 1997, Machine Learning.
[56] Marimuthu Palaniswami,et al. Computational Intelligence: A Dynamic System Perspective , 1995 .
[57] K. Chaloner,et al. Bayesian Experimental Design: A Review , 1995 .
[58] Miodrag Potkonjak,et al. Power efficient organization of wireless sensor networks , 2001, ICC 2001. IEEE International Conference on Communications. Conference Record (Cat. No.01CH37240).
[59] Uri Lerner,et al. Inference in Hybrid Networks: Theoretical Limits and Practical Algorithms , 2001, UAI.
[60] Andreas Krause,et al. Intelligent light control using sensor networks , 2005, SenSys '05.
[61] Xavier Boyen,et al. Tractable Inference for Complex Stochastic Processes , 1998, UAI.
[62] Nicholas Roy,et al. Efficient Optimization of Information-Theoretic Exploration in SLAM , 2008, AAAI.
[63] H. Robbins. Some aspects of the sequential design of experiments , 1952 .
[64] Andrew McCallum,et al. Conditional Random Fields: Probabilistic Models for Segmenting and Labeling Sequence Data , 2001, ICML.
[65] Sanjoy Dasgupta,et al. Coarse sample complexity bounds for active learning , 2005, NIPS.
[66] J. Ross Quinlan,et al. Induction of Decision Trees , 1986, Machine Learning.
[67] Wolfram Burgard,et al. Information Gain-based Exploration Using Rao-Blackwellized Particle Filters , 2005, Robotics: Science and Systems.
[68] Andreas Krause,et al. Near-Optimal Sensor Placements in Gaussian Processes: Theory, Efficient Algorithms and Empirical Studies , 2008, J. Mach. Learn. Res..
[69] J. K. Kinnear,et al. Advances in Genetic Programming , 1994 .
[70] Andreas Krause,et al. Optimal Nonmyopic Value of Information in Graphical Models - Efficient Algorithms and Theoretical Limits , 2005, IJCAI.