IJCNN International Joint Conference on Neural Networks, Beijing, China, Vol. 1, Publishing House of Electronics Industry, 1992.
Palavraschave: learning, transducer, finite state
297  Learning Internal Representations by Error Propagation
D. E. Rumelhart and G. E. Hinton and R. J. Williams
Parallel Distributed Processing, MIT Press, 1986.
Palavraschave: learning, error, error prpagation

298 Learning Large DeBruijn Automata with FeedForward Neural Networks
D. S. Clouse and C. L. Giles and B. G. Horne and G. W. Cottrell
Technical Report, Computer Science and Engineering, University of California at San Diego, Number CS94398, 1994.
Palavraschave: learning, automata feed forward neural net

299 Learning longterm dependencies is not as difficult with NARX recurrent neural networks
T. Lin and B. G. Horne and P. Tino and C. L. Giles
Technical Report, Institute for Advanced Computer Studies, University of Maryland, Number UMIACSTR9578 and CSTR3500, 1995.
Palavraschave: learning, NARX, dependencies

300  Learning LongTerm Dependencies with Gradient Descent is Difficult
Yoshua Bengio and P. Simard and P. Frasconi
IEEE Transactions on Neural Networks, 5(2), pp. 157166, 1994.
Palavraschave: learning, gradiend descent, training

301 Learning Recursive Distributed Representations for Holistic Computation
Lonnie Chrisman
Connection Science, 3(4), pp. 345366, 1991.
Palavraschave: learning, recursive, holistic computations

302 Learning Sequential Structure with the Realtime Recurrent Learning Algorithm
Anthony W. Smith and David Zipster
International Journal of Neural Systems, 1(2), pp. 125131, 1989.
Palavraschave: learning, sequential structure, real time learning

303 Learning state space trajectories in recurrent neural networks
Barak Pearlmutter
Neural Computation, 1(2), pp. 263269, 1989.
Palavraschave: learning, state space trajectories

304 Learning System Architetures Composed of Multiple Learning Modules
D. L. Reilly and C. Scofield and C. Elbaum and L. N. Cooper
Proceedings of the IEEE First International Conference On Neural Networks, 1987.
Palavraschave: learning, architetures, multiple learning

305 Learning the Past Tense of English Verbs using Recurrent Neural Networks
L. R. Leerink and M. Jabri
Australian Conference on Neural Networks, pp. 222226, Australian National University, 1996.
Palavraschave: learning, english, verbs, grammar

306  Learning with product units
L. R. Leerink and C. L. Giles and B. G. Horne and M. A. Jabri
Advances in Neural Information Processing Systems 7, MIT Press.
Palavraschave: learning, information

307 Learning, Representation, and Synthesis of Automata and Sequential Machines in Recurrent Neural Networks
C. Lee Giles
AAAI96 Fall Symposium Series Workshop on Learning Complex Behaviors in Adaptive Intelligent Systems, pp. 132139, 1996.
Palavraschave: learning, representation, syntesis automata

308 Long Short Term Memory
S . Hochreiter and J. Schmidhuber
Neural Computation.
Palavraschave: memory,

309 Long Term Memory Storage Capacity of Multiconnected Neural Networks
P. Peretto and J. J. Niez
Biological Cybernetics, Vol. 54, p. 53, 1986.
Palavraschave: memory, storage, multiconnection

310 Learning the Past Tense of English Verbs using Recurrent Neural Networks
L. R. Leerink and M. Jabri
Australian Conference on Neural Networks, pp. 222226, Australia National University, 1996.
Palavraschave: learning, english, grammar

311 Learning, Representation, and Synthesis of Automata and Sequential Machines in Recurrent Neural Networks
C. Lee Giles
AAAI96 Fall Symposium Series Workshop on Learning Complex Behaviors in Adaptive Intelligent Systems, pp. 132139, 1996.
Palavraschave: learning, sntesis, sequntial machines

312 Learning longterm dependencies is not as difficult with NARX recurrent neural networks
T. Lin and B. G. Horne and P. Tino and C. L. Giles
Advances in Neural Information Processing Systems 8, p. 577, MIT Press, 1996.
Palavraschave: learning, NARX neural net, dependencies

313  Learning LongTerm Dependencies with Gradient Descent is Difficult
Yoshua Bengio and P. Simard and P. Frasconi
IEEE Transactions on Neural Networks, 5(2), pp. 157166, 1994.
Palavraschave: learning, gradient method, training

314 Machine Learning Using a Higher Order Correlational Network
Y. C. Lee and G. Doolen and H. H. Chen and G. Z. Sun and T. Maxwell and H. Y. Lee and C. L. Giles
Physica D, Vol. 22D, Number 13, pp. 276306, 1986.
Palavraschave: machine learning, learning,

315 Memory Neuron Networks for Identification and Control of Dynamical Systems
P. S. Sastry and G. Santharam and K. P. Unnikrishnan
IEEE Transactions on Neural Networks, 5(2), pp. 306319, 1994.
Palavraschave: memory, identification, control

316 Memorybased Reinforcement Learning: Efficient Computation with Prioritized Sweeping
A. W. Moore and C. G. Atkeson
Advances in Neural Information Processing Systems 5, pp. 263270, Morgan Kaufmann Publishers, 1993.
Palavraschave: memory, reinforced learning ,learning

314  Modular Recurrent Neural Networks for Mandarin Syllable Recognition
S.H. Chen and Y.F. Liao
IEEENN, 9(6), p. 1430, November 1998.
Palavraschave: pattern recognition, pattern, mandarin

315 Making the World Differentiable: On Using Supervised Learning Fully Recurrent Neural Networks for Dynamic Reinforcement Learning and Planning in NonStationary Environments
J. H. Schmidhuber
Technical Report, Technische Universität München, 1990.
Palavraschave: learning, planning, non stationary envirolments

316 Natural Language Grammatical Inference: A Comparison of Recurrent Neural Networks and Machine Learning Methods
Steve Lawrence and Sandiway Fong and C. Lee Giles
Symbolic, Connectionist, and Statistical Approaches to Learning for Natural Language Processing, Lecture notes in AI, pp. 3347, SpringerVerlag, 1996.
Palavraschave: grammar, language, machine learning

317 Neural Network Learning and Expert Systems
S. I. Gallant
MIT Press, 1993.
Palavraschave: learning, expert systems

318  Neural Networks for control
E. Sontag
Essays on Control: Perspectives in the Theory and its Applications, pp. 339380, Birkhauser, 1993.
Palavraschave: control

319 Neural Networks with External Memory Stack that Learn ContextFree Grammars from Examples
G. Z. Sun and H. H. Chen and C. L. Giles and Y. C. Lee and D. Chen
Proceedings of the Conference on Information Science and Systems, Vol. II, p. 649, 1990.
Palavraschave: memory, learning,

320  Noisy Recurrent Neural Networks: The ContinuousTime Case
S. Das and O. Olurotimi
IEEENN, 9(5), p. 913, September 1998.
Palavraschave: noise , recurrente

321  Nonlinear Prediction of Speech Signals Using Memory Neuron Networks
P. Poddar and K. P. Unnikrishnan
Neural Networks for Signal Processing: Proceedings of the 1991 IEEE Workshop, pp. 395404, IEEE Press, 1991.
Palavraschave: nonlinear prediction, speech, memory

322 NonLiteral Transfer of Information among Inductive Learners
L. Y. Pratt
Neural Networks: Theory and Applications II, Academic Press, 1992.
Palavraschave: learning, indutive leaners

323  Natural Language Grammatical Inference: A Comparison of Recurrent Neural Networks and Machine Learning Methods
Steve Lawrence and Sandiway Fong and C. Lee Giles
Symbolic, Connectionist, and Statistical Approaches to Learning for Natural Language Processing, Lecture notes in AI, pp. 3347, Springer Verlag, 1996.
Palavraschave: grammar, learning, training

324  On Recurrent Neural Networks and Representing Finite State Recognizers
M. W. Goudreau and C. Lee Giles and S. T. Chakradhar and D. Chen
Proceedings of the Third International Conference on Neural Networks, May 1993.
Palavraschave: recognizers, finite state

325 On the Applicability of Neural Network and Machine Learning Methodologies to Natural Language Processing
S. Lawrence and S. Fong and C. L. Giles
Technical Report, Institute for Advanced Computer Studies, University of Maryland, Number UMIACSTR9564 and CSTR3479, June 1995.
Palavraschave: machine learning, methodologies, language

326 On the Computational Power of Neural Nets
H. T. Siegelmann and E. D. Sontag
Proceedings of the Fifth ACM Workshop on Computational Learning Theory, pp. 440449, ACM, 1992.
Palavraschave: computational power, lerning

327 On the Effect of Analog Noise in DiscreteTime Analog Computations
Wolfgang Maass and Pekka Orponen
Neural Computation, Vol. 10, pp. 10711095, 1998.
Palavraschave: noise, discrete time systems

328 On the NonExistence of a Unversal Learning Algorithm for Recurrent Neural Networks
H. Wiklicky
Advances in Neural Information Processing Systems 6, pp. 431436, Morgan Kaufmann, 1994.
Palavraschave: learning,

329 OnLine Training of Recurrent Neural Networks with Continuous Topology Adaptation
D. Obradovic
IEEE Transactions on Neural Networks, 7(1), pp. 222228, January 1996.
Palavraschave: trainig, learning, continuous topology adaptation

330 On the Applicability of Neural Network and Machine Learning Methodologies to Natural Language
Steve Lawrence and C. Lee Giles and Sandiway Fong
Processing Technical Report, Institute for Advanced Computer Studies, University of Maryland, College Park MD 20742, Number UMIACSTR9564 and CSTR3479, 1995.
Palavraschave: learning, language , method

331 On the NonExistence of a Unversal Learning Algorithm for Recurrent Neural Networks
H. Wiklicky
Advances in Neural Information Processing Systems 6, pp. 431436, Morgan Kaufmann, 1994.
Palavraschave: learning,

332 Properties of Neural Networks with Applications to Modelling Nonlinear Dynamical Systems
S. A. Billings and H. B. Jamaluddin and S. Chen
International Journal of Control, 55(1), pp. 193224, 1992.
Palavraschave:non linera systems, dynamic systems, proprieties

333 Pruning Recurrent Neural Networks for Improved Generalization Performance
C. Lee Giles and Christian W. Omlin
IEEE Transactions on Neural Networks, 5(5), pp. 848851, September 1994.
Palavraschave: pruning

334 Pruning Recurrent Neural Networks for Improved Generalization Performance
C. W. Omlin and C. L. Giles
Technical Report, Rensselaer Polytechnic Institute, Number TR 936, April 1993.
Palavraschave: pruning

335 Pruning Recurrent Neural Networks for Improved Generalization Performance
C. W. Omlin and C. Lee Giles
IEEE Transactions on Neural Networks, 1995.
Palavraschave: pruning, generalization,

336  Pruning Recurrent Neural Networks for Improved Generalization Performance
Christian W. Omlin and C. Lee Giles
Technical Report, Rensselaer Polytechnic Institute, Computer Science Department, Number 936, April 1993.
Palavraschave:pruning, generalization

337 Random DFA's can be Approximately Learned from Sparse Uniform Examples
K. J. Lang
Proceedings of the Fifth ACM Workshop on Computational Learning Theory, pp. 4552, ACM, 1992.
Palavraschave: learning, DFA

338 Recurrent Networks: Second Order Properties and Pruning
M. W. Pedersen and L. K. Hansen
Advances in Neural Information Processing Systems 7, MIT Press, 1995.
Palavraschave: pruning, second order properties

339 Recurrent Neural Networks and Robust Time Series Prediction
Jerome T. Connor and R. Douglas Martin and L. E. Atlas
IEEE Transactions on Neural Networks, 5(2), pp. 240254, March 1994.
Palavraschave: time series, prediction, robust prediction

340 Recurrent Neural Networks for Adaptive Temporal Processing
Yoshua Bengio and P. Frasconi and M. Gori and G. Soda
Proceedings of the 6th Italian Workshop on Parallel Architectures and Neural Networks WIRN93, pp. 85117, World Scientific Pub., 1993.
Palavraschave: adaptive systems, processing , temporal, processing

341 Recurrent Neural Networks, ContextFree Grammars, and Evolution
John Batali
Technical Report, Department of Cognitive Science, University of California at San Diego, 1995.
Palavraschave: grammars, evotution, free grammars

342 Recurrent Neural Networks, Hidden Markov Models and Stochastic Grammars
G. Z. Sun and H. H. Chen and Y. C. Lee and C. L. Giles
International Joint Conference on Neural Networks, San Diego, 1990, Vol. I, pp. 729734, IEEE Press, 1990.
Palavraschave: markov model, grammars, stocastic grammars

343 Refinement of Approximate ReasoningBased Controllers By Reinforcement Learning
H. R. Berenji
Machine Learning, Proceedings of the Eighth International International Workshop, p. 475, Morgan Kaufmann Publishers, 1991.
Palavraschave: learning, machine learning, controlers

344 Refining Algorithms with KnowledgeBased Neural Networks: Improving the ChouFasman Algorithm for Protein Folding
R. Maclin and J. W. Shavlik
Computational Learning Theory and Natural Learning Systems, MIT Press, 1992.
Palavraschave: ChouFasman algorithm, learning, biomedic

345 Regression Modeling in BackPropagation and Projection Pursuit Learning
JenNeng Hwang and ShyhRong Lay and Martin Maechler and R. Douglas Martin and James Schimert
IEEE Transactions on Neural Networks, 5(3), pp. 342353, 1994.
Palavraschave: regression, back propagation, learning

346  Remembering the Past: The Role of Embedded Memory in Recurrent Neural Network Architectures
C. L. Giles and T. Lin and B. G. Horne
Neural Networks for Signal Processing VII, Proceedings of The 1997 IEEE Workshop, IEEE Press, 1997.
Palavraschave: memory, embebed memory

347 Representation and Learning in Recurrent Neural Network Architectures,
C. L. Giles and B. G. Horne
Proceedings of the Eighth Yale Workshop on Adaptive and Learning Systems, 1994.
Palavraschave: learning, architeture, representation

348 Representation of Finite State Automata in Recurrent Radial Basis Function Networks
P. Frasconi and M. Gori and M. Maggini and G. Soda
Machine Learning, Vol. 23, pp. 532, 1996.
Palavraschave: finite state, automata, radial base functions

349 Recurrent Neural Networks, Hidden Markov Models and Stochastic Grammars
G. Z. Sun and H. H. Chen and Y. C. Lee and C. Lee Giles
International Joint Conference on Neural Networks, San Diego, 1990, Vol. I, pp. 729734, IEEE Press, 1990.
Palavraschave: markov, grammar, stocastic models

350 Recurrent Neural Networks and Prior Knowledge for Sequence Processing: A Constrained Nondeterminstic Approach
P. Frasconi and M. Gori and G. Soda
KnowledgeBased Systems, 8(6), pp. 313332, 1995.
Palavraschave: sequence processing, non deterministic, constrained

351 Recurrent Neural Networks Can Be Trained to Be Maximum A Posteriori Probability Classifiers
S. Santini and Del A. Bimbo
Neural Networks, 8(1), pp. 2529, 1995.
Palavraschave: Training, probability, classifier

352 Recurrent Neural Networks Can Learn to Implement SymbolSensitive Counting
Paul Rodriguez and Janet Wiles
Advances in Neural Information Processing Systems, Vol. 10, The MIT Press, 1998.
Palavraschave: symbol sensitive,

353 Recurrent Neural Networks, ContextFree Grammars, and Evolution
John Batali
Technical Report, Department of Cognitive Science, University of California at San Diego, 1995.
Palavraschave: grammars, evolution

354 Recurrent neural networks: A functional approach
B. G. Horne
PhD Thesis, University of New Mexico, May 1993.
Palavraschave: recurrent net

355 Representation of Fuzzy Finitestate Automata in Continuous Recurrent Neural Networks
C. W. Omlin and K. K. Thornber and C. L. Giles
IEEE International Conference on Neural Networks (ICNN'96), p. 1023, IEEE Press, 1996.
Palavraschave: fuzzy, finite automata, continuous neural net.

356 Rule Checking with Recurrent Neural Networks
C. W. Omlin and C. L. Giles
IEEE Transactions on Knowledge and Data Engineering, 1995.
Palavraschave: control, rule checking

357 Rule Inference for Financial Prediction using Recurrent Neural Networks
C. Lee Giles and Steve Lawrence and A. C. Tsoi
Proceedings of IEEE/IAFE Conference on Computational Intelligence for Financial Engineering (CIFEr), pp. 253259, IEEE, 1997.
Palavraschave: prediction, finance, rule

358 Rule Refinement with Recurrent Neural Networks
C. L. Giles and C. W. Omlin
1993 IEEE International Conference on Neural Networks (ICNN'93), Vol. II, p. 810, IEEE Press, 1993.
Palavraschave: rule, refinement

359 Second Order Derivatives for Network Pruning: Optimal Brain Surgeon
Babak Hassibi and David G. Stork
Advances in Neural Information Processing Systems 5, Morgan Kaufmann Publishers, 1993.

360  Sequence Learning with Incremental HigherOrder Neural Networks
Mark B. Ring
Technical Report, Artificial Intelligence Laboratory, University of Texas at Austin, Number AI 93193, January 1993.
Palavraschave: learning, higher order neural net

361 Sequential Behavior and Learning in Evolved Dynamical Neural Networks
B. M. Yamauchi and R. D. Beer
Adaptive Behavior, 2(3), pp. 219246, 1994.
Palavraschave: learning, dynamic systems, sequential 
Share with your friends: 