Neural Network Exploration Using Optimal Experiment Design

[1]  Mohamad Hisham Choueiki.  A designed experiment on the use of neural-network models in short-term hourly load forecasting / , 1995 .

[2]  Andrew G. Barto,et al.  Learning to Act Using Real-Time Dynamic Programming , 1995, Artif. Intell..

[3]  S. Hochreiter,et al.  REINFORCEMENT DRIVEN INFORMATION ACQUISITION IN NONDETERMINISTIC ENVIRONMENTS , 1995 .

[4]  David A. Cohn,et al.  Active Learning with Statistical Models , 1996, NIPS.

[5]  W. Näther.  Optimum experimental designs , 1994 .

[6]  Sollich.  Query construction, entropy, and generalization in neural-network models. , 1994, Physical review. E, Statistical physics, plasmas, fluids, and related interdisciplinary topics.

[7]  Wray L. Buntine,et al.  Computing second derivatives in feed-forward networks: a review , 1994, IEEE Trans. Neural Networks.

[8]  S. Schaal,et al.  Robot juggling: implementation of memory-based learning , 1994, IEEE Control Systems.

[9]  Barak A. Pearlmutter.  Fast Exact Multiplication by the Hessian , 1994, Neural Computation.

[10]  Gerhard Paass,et al.  Bayesian Query Construction for Neural Network Models , 1994, NIPS.

[11]  Michael I. Jordan,et al.  Supervised learning from incomplete data via an EM approach , 1993, NIPS.

[12]  Mark Plutowski,et al.  Selecting concise training sets from clean data , 1993, IEEE Trans. Neural Networks.

[13]  H. Sebastian Seung,et al.  Information, Prediction, and Query by Committee , 1992, NIPS.

[14]  David Haussler,et al.  Decision Theoretic Generalizations of the PAC Model for Neural Net and Other Learning Applications , 1992, Inf. Comput..

[15]  Michael I. Jordan,et al.  Forward Models: Supervised Learning with a Distal Teacher , 1992, Cogn. Sci..

[16]  David J. C. MacKay,et al.  Information-Based Objective Functions for Active Data Selection , 1992, Neural Computation.

[17]  Gerald Tesauro,et al.  How Tight Are the Vapnik-Chervonenkis Bounds? , 1992, Neural Computation.

[18]  Elie Bienenstock,et al.  Neural Networks and the Bias/Variance Dilemma , 1992, Neural Computation.

[19]  Sebastian Thrun,et al.  The role of exploration in learning control , 1992 .

[20]  Sebastian Thrun,et al.  Active Exploration in Dynamic Environments , 1991, NIPS.

[21]  Donald F. Specht,et al.  A general regression neural network , 1991, IEEE Trans. Neural Networks.

[22]  Steven J. Nowlan,et al.  Soft competitive adaptation: neural network learning algorithms based on fitting statistical mixtures , 1991 .

[23]  Phillip J. McKerrow,et al.  Introduction to robotics , 1991 .

[24]  Geoffrey E. Hinton,et al.  Adaptive Mixtures of Local Experts , 1991, Neural Computation.

[25]  Eric B. Baum,et al.  Constructing Hidden Units Using Examples and Queries , 1990, NIPS.

[26]  Bartlett W. Mel.  Connectionist Robot Motion Planning: A Neurally-Inspired Approach to Visually-Guided Reaching , 1990 .

[27]  Ronald L. Rivest,et al.  On the sample complexity of pac-learning using random and chosen examples , 1990, Annual Conference Computational Learning Theory.

[28]  Jenq-Neng Hwang,et al.  Query learning based on boundary search and gradient computation of trained multilayer perceptrons , 1990, 1990 IJCNN International Joint Conference on Neural Networks.

[29]  Brian Armstrong,et al.  On Finding Exciting Trajectories for Identification Experiments Involving Systems with Nonlinear Dynamics , 1989, Int. J. Robotics Res..

[30]  David Haussler,et al.  Learnability and the Vapnik-Chervonenkis dimension , 1989, JACM.

[31]  D. M. Titterington,et al.  Recent advances in nonlinear experiment design , 1989 .

[32]  David A. Cohn,et al.  Training Connectionist Networks with Queries and Selective Sampling , 1989, NIPS.

[33]  David Haussler,et al.  What Size Net Gives Valid Generalization? , 1989, Neural Computation.

[34]  Geoffrey J. McLachlan,et al.  Mixture models : inference and applications to clustering , 1989 .

[35]  M Kuperstein,et al.  Neural model of adaptive hand-eye coordination for single postures. , 1988, Science.

[36]  Tony Scallan,et al.  Elements of Statistical Computing: Numerical Computation , 1988 .

[37]  W. Cleveland,et al.  Regression by local fitting: Methods, properties, and computational algorithms , 1988 .

[38]  George E. P. Box,et al.  Empirical Model‐Building and Response Surfaces , 1988 .

[39]  Ronald A. Thisted,et al.  Elements of statistical computing , 1986 .

[40]  Leslie G. Valiant,et al.  A theory of the learnable , 1984, STOC '84.

[41]  Dana Angluin,et al.  A Note on the Number of Queries Needed to Identify Regular Languages , 1981, Inf. Control..

[42]  W. J. Studden,et al.  Theory Of Optimal Experiments , 1972 .