An Artificial Experimenter for Enzymatic Response Characterisation
暂无分享,去创建一个
[1] G. A. Montgomery,et al. Ultraviolet absorption spectra of DPN and analogs of DPN. , 1959, Archives of biochemistry and biophysics.
[2] R. Fisher. Statistical methods for research workers , 1927, Protoplasma.
[3] T. C. CHAMBERLIN. The Method of Multiple Working Hypotheses , 1931, The Journal of Geology.
[4] Jonathan S. Lindsey,et al. A two-tiered strategy for simplex and multidirectional optimization of reactions with an automated chemistry workstation , 2002 .
[5] Klaus-Peter Zauner,et al. Self-Adaptive Scouting - Autonomous Experimentation for Systems Biology , 2004, EvoWorkshops.
[6] Peter Auer,et al. Using Confidence Bounds for Exploitation-Exploration Trade-offs , 2003, J. Mach. Learn. Res..
[7] P. Langley,et al. Computational Models of Scientific Discovery and Theory Formation , 1990 .
[8] Anders Krogh,et al. Neural Network Ensembles, Cross Validation, and Active Learning , 1994, NIPS.
[9] Pierre Baldi,et al. Bayesian surprise attracts human attention , 2005, Vision Research.
[10] J. March. Exploration and exploitation in organizational learning , 1991, STUDI ORGANIZZATIVI.
[11] Peter C. Cheeseman,et al. Onboard Science Data Analysis: Applying Data Mining to Science-Directed Autonomy , 1998, IEEE Intell. Syst..
[12] Yang Li,et al. Analysis of Tiling Microarray Data by Learning Vector Quantization and Relevance Learning , 2007, IDEAL.
[13] F. J. Anscombe,et al. Graphs in Statistical Analysis , 1973 .
[14] Leo Breiman,et al. Bagging Predictors , 1996, Machine Learning.
[15] Jonathan S. Lindsey,et al. A parallel simplex search method for use with an automated chemistry workstation , 2002 .
[16] Tom Addis,et al. A Simulation of Model-Based Reasoning about Disparate Phenomena , 1999 .
[17] Klaus-Peter Zauner,et al. Integration of Cellular Biological Structures Into Robotic Systems , 2009 .
[18] R. L. Eubank. A Simple Smoothing Spline , 1994 .
[19] David A. Cohn,et al. Training Connectionist Networks with Queries and Selective Sampling , 1989, NIPS.
[20] H. Sebastian Seung,et al. Query by committee , 1992, COLT '92.
[21] Shlomo Argamon,et al. Minimizing Manual Annotation Cost in Supervised Training from Corpora , 1996, ACL.
[22] Deepayan Chakrabarti,et al. Multi-armed bandit problems with dependent arms , 2007, ICML '07.
[23] Sebastian Thrun,et al. Active Exploration in Dynamic Environments , 1991, NIPS.
[24] W. Madych,et al. Polyharmonic cardinal splines , 1990 .
[25] Stephen Muggleton,et al. Combining Inductive Logic Programming, Active Learning and Robotics to Discover the Function of Genes , 2001, Electron. Trans. Artif. Intell..
[26] Saibal Roy,et al. A micro electromagnetic generator for vibration energy harvesting , 2007 .
[27] Herbert A. Simon,et al. Experimentation in machine discovery , 1990 .
[28] A. Lehninger. Principles of Biochemistry , 1984 .
[29] K. Salkauskas. $C^1$ >splines for interpolation of rapidly varying data , 1984 .
[30] Partha Niyogi,et al. Active Learning for Function Approximation , 1994, NIPS.
[31] Robert D. Nowak,et al. Faster Rates in Regression via Active Learning , 2005, NIPS.
[32] Ian Sinclair,et al. Designing Committees of Models through Deliberate Weighting of Data Points , 2003, J. Mach. Learn. Res..
[33] R. Castano,et al. Onboard Autonomous Rover Science , 2007, 2007 IEEE Aerospace Conference.
[34] G. Wahba. Smoothing noisy data with spline functions , 1975 .
[35] Ross D King,et al. Intelligent software for laboratory automation. , 2004, Trends in biotechnology.
[36] P. Davies,et al. Approximating data with weighted smoothing splines , 2007, 0712.1692.
[37] Nasser M. Nasrabadi,et al. Pattern Recognition and Machine Learning , 2006, Technometrics.
[38] Sebastian Thrun,et al. Exploration in active learning , 1998 .
[39] Jonathan S. Lindsey,et al. An approach for parallel and adaptive screening of discrete compounds followed by reaction optimization using an automated chemistry workstation , 2002 .
[40] Raúl E. Valdés-Pérez,et al. Machine Discovery of Chemical Reaction Pathways , 1991, AI Mag..
[41] Jeffrey O. Pfaffmann,et al. Scouting context-sensitive components , 2001, Proceedings Third NASA/DoD Workshop on Evolvable Hardware. EH-2001.
[42] Burr Settles,et al. Active Learning Literature Survey , 2009 .
[43] A. Atkinson,et al. The design of experiments for discriminating between two rival models , 1975 .
[44] P. Hall,et al. Sequential methods for design-adaptive estimation of discontinuities in regression curves and surfaces , 2003 .
[45] Klaus-Peter Zauner,et al. Molecular Information Technology , 2005 .
[46] Hod Lipson,et al. Distilling Free-Form Natural Laws from Experimental Data , 2009, Science.
[47] Thomas G. Dietterich,et al. Editors. Advances in Neural Information Processing Systems , 2002 .
[48] Anne Lohrli. Chapman and Hall , 1985 .
[49] H. Sebastian Seung,et al. Selective Sampling Using the Query by Committee Algorithm , 1997, Machine Learning.
[50] Jean-Christophe Plouvier,et al. Experiment planner for strategic experimentation with an automated chemistry workstation , 1992 .
[51] Jan M. Żytkow,et al. Discovering quarks and hidden structure , 1991 .
[52] Ross D. King,et al. Active Learning for Regression Based on Query by Committee , 2007, IDEAL.
[53] A. P. de Silva,et al. Molecular logic and computing. , 2007, Nature nanotechnology.
[54] T. C. Chamberlin. The Method of Multiple Working Hypotheses , 1931, The Journal of Geology.
[55] Amanda Clare,et al. An ontology for a Robot Scientist , 2006, ISMB.
[56] L. Andrew Corkan,et al. Experiment manager software for an automated chemistry workstation, including a scheduler for parallel experimentation , 1992 .
[57] Masashi Sugiyama,et al. Active Learning with Model Selection in Linear Regression , 2008, SDM.
[58] Archie C. Chapman,et al. ε-first policies for budget-limited multi-armed bandits , 2010, AAAI 2010.
[59] David A. Cohn,et al. Active Learning with Statistical Models , 1996, NIPS.
[60] H. Chernoff. Sequential Design of Experiments , 1959 .
[61] R. L. Eubank,et al. A simple smoothing spline, III , 2004, Comput. Stat..
[62] G. Seelig,et al. Enzyme-Free Nucleic Acid Logic Circuits , 2022 .
[63] Klaus-Peter Zauner,et al. Scouting enzyme behavior , 2002, Proceedings of the 2002 Congress on Evolutionary Computation. CEC'02 (Cat. No.02TH8600).
[64] H. Robbins. Some aspects of the sequential design of experiments , 1952 .
[65] A. E. Hoerl,et al. Ridge regression: biased estimation for nonorthogonal problems , 2000 .
[66] Jon Williamson,et al. The Philosophy of Science and its relation to Machine Learning , 2009, Scientific Data Mining and Knowledge Discovery.
[67] R. Agrawal. Sample mean based index policies by O(log n) regret for the multi-armed bandit problem , 1995, Advances in Applied Probability.
[68] M Conrad,et al. Enzymatic Computing , 2001, Biotechnology progress.
[69] Bernhard Schölkopf,et al. A tutorial on support vector regression , 2004, Stat. Comput..
[70] Leonard G. C. Hamey,et al. Minimisation of data collection by active learning , 1995, Proceedings of ICNN'95 - International Conference on Neural Networks.
[71] Robert Tibshirani,et al. The Elements of Statistical Learning: Data Mining, Inference, and Prediction, 2nd Edition , 2001, Springer Series in Statistics.
[72] L. Andrew Corkan,et al. A planning module for performing grid search, factorial design, and related combinatorial studies on an automated chemistry workstation , 1999 .
[73] Fredrik Olsson,et al. A literature survey of active machine learning in the context of natural language processing , 2009 .
[74] David Gooding,et al. Experiment and the Making of Meaning , 1990 .
[75] Christopher H. Bryant,et al. Functional genomic hypothesis generation and experimentation by a robot scientist , 2004, Nature.
[76] Gunnar Rätsch,et al. Active Learning with Support Vector Machines in the Drug Discovery Process , 2003, J. Chem. Inf. Comput. Sci..
[77] Ken E. Whelan,et al. The Automation of Science , 2009, Science.
[78] David J. C. MacKay,et al. Information-Based Objective Functions for Active Data Selection , 1992, Neural Computation.
[79] Jan M. Zytkow,et al. Constructing Models of Hidden Structure , 1991, ISMIS.
[80] Tony Hey,et al. The Fourth Paradigm: Data-Intensive Scientific Discovery , 2009 .
[81] Sebastian Thrun,et al. The role of exploration in learning control , 1992 .
[82] C. Buck,et al. Popper's philosophy for epidemiologists. , 1975, International journal of epidemiology.
[83] Naoki Abe,et al. Query Learning Strategies Using Boosting and Bagging , 1998, ICML.
[84] Peter Ruoff,et al. Computer Controlled Automated Assay for Comprehensive Studies of Enzyme Kinetic Parameters , 2010, PloS one.
[85] A. Atkinson,et al. Optimal design : Experiments for discriminating between several models , 1975 .
[86] C. Reinsch. Smoothing by spline functions , 1967 .
[87] J. S. Hunter,et al. Statistics for Experimenters: Design, Innovation, and Discovery , 2006 .
[88] Jan M. Zytkow,et al. Automated Discovery of Empirical Equations from Data , 1991, ISMIS.
[89] Carl de Boor,et al. A Practical Guide to Splines , 1978, Applied Mathematical Sciences.
[90] R. Schapire. The Strength of Weak Learnability , 1990, Machine Learning.
[91] Vladimir N. Vapnik,et al. The Nature of Statistical Learning Theory , 2000, Statistics for Engineering and Information Science.
[92] R. A. Fisher,et al. Design of Experiments , 1936 .
[93] Shensheng Zhang,et al. Active Learning with Ensembles for DOE , 2005 .
[94] Douglas C. Montgomery,et al. Response Surface Methodology: Process and Product Optimization Using Designed Experiments , 1995 .
[95] John N. Tsitsiklis,et al. Active Learning Using Arbitrary Binary Valued Queries , 1993, Machine Learning.
[96] Andrew McCallum,et al. Employing EM and Pool-Based Active Learning for Text Classification , 1998, ICML.
[97] Klaus-Peter Zauner,et al. Molecular approach to informal computing , 2001, Soft Comput..
[98] G. Wahba. Improper Priors, Spline Smoothing and the Problem of Guarding Against Model Errors in Regression , 1978 .
[99] Richard S. Sutton,et al. Reinforcement Learning: An Introduction , 1998, IEEE Trans. Neural Networks.
[100] P E Brodelius. Enzyme assays. , 1991, Current opinion in biotechnology.
[101] I J Schoenberg,et al. SPLINE FUNCTIONS AND THE PROBLEM OF GRADUATION. , 1964, Proceedings of the National Academy of Sciences of the United States of America.
[102] R. A. Leibler,et al. On Information and Sufficiency , 1951 .
[103] Mark Woods,et al. Developing an autonomous science capability for european mars missions , 2008, ICRA 2008.
[104] Andrew W. Moore,et al. Reinforcement Learning: A Survey , 1996, J. Artif. Intell. Res..
[105] Carl E. Rasmussen,et al. Gaussian processes for machine learning , 2005, Adaptive computation and machine learning.
[106] William A. Gale,et al. A sequential algorithm for training text classifiers , 1994, SIGIR '94.
[107] Steve Chien,et al. Enhancing Science and Automating Operations using Onboard Autonomy , 2006 .
[108] Jan M. Zytkow,et al. Automated Discovery: A Fusion of Multidisciplinary Principles , 2000, Canadian Conference on AI.
[109] George E. P. Box,et al. Empirical Model‐Building and Response Surfaces , 1988 .
[110] G. R. Hext,et al. Sequential Application of Simplex Designs in Optimisation and Evolutionary Operation , 1962 .
[111] Steven A. Orszag,et al. CBMS-NSF REGIONAL CONFERENCE SERIES IN APPLIED MATHEMATICS , 1978 .
[112] M. E. Grismer,et al. FIELD SENSOR NETWORKS AND AUTOMATED MONITORING OF SOIL WATER SENSORS , 1992 .
[113] Kun Deng,et al. Balancing exploration and exploitation: a new algorithm for active machine learning , 2005, Fifth IEEE International Conference on Data Mining (ICDM'05).
[114] Wei-Min Shen. Discovery as autonomous learning from the environment , 2004, Machine Learning.
[115] G. Wahba. Spline models for observational data , 1990 .
[116] Jonathan S. Lindsey,et al. An experiment planner for performing successive focused grid searches with an automated chemistry workstation , 2002 .
[117] Jieming Zhu,et al. Automated Discovery in a Chemistry Laboratory , 1990, AAAI.