Hardness results for neural network approximation problems

[1]  Shai Ben-David,et al.  On the difficulty of approximately maximizing agreements , 2000, J. Comput. Syst. Sci..

[2]  Shai Ben-David,et al.  On the Di cultyof Approximately Maximizing Agreements , 2000 .

[3]  Van H. Vu On the Infeasibility of Training Neural Networks with Small Squared Errors , 1997, NIPS.

[4]  L. K. Jones,et al.  The computational intractability of training sigmoidal neural networks , 1997, IEEE Trans. Inf. Theory.

[5]  Sanjeev Khanna,et al.  On the Hardness of Approximating Max k-Cut and its Dual , 1997, Chic. J. Theor. Comput. Sci..

[6]  Peter L. Bartlett,et al.  Efficient agnostic learning of neural networks with bounded fan-in , 1996, IEEE Trans. Inf. Theory.

[7]  Hava T. Siegelmann,et al.  On the complexity of training neural networks with continuous activation functions , 1995, IEEE Trans. Neural Networks.

[8]  Carsten Lund,et al.  On the hardness of approximating minimization problems , 1994, JACM.

[9]  Pascal Koiran,et al.  Efficient learning of continuous neural networks , 1994, COLT '94.

[10]  Leslie G. Valiant,et al.  Cryptographic limitations on learning Boolean formulae and finite automata , 1994, JACM.

[11]  Sanjeev Arora,et al.  The Hardness of Approximate Optimia in Lattices, Codes, and Systems of Linear Equations , 1993, FOCS.

[12]  G. Lugosi,et al.  Strong Universal Consistency of Neural Network Classifiers , 1993, Proceedings. IEEE International Symposium on Information Theory.

[13]  Erez Petrank,et al.  The hardness of approximation: Gap location , 1993, [1993] The 2nd Israel Symposium on Theory and Computing Systems.

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

[15]  Hans Ulrich Simon,et al.  Robust Trainability of Single Neurons , 1995, J. Comput. Syst. Sci..

[16]  ERIC B. BAUM,et al.  On learning a union of half spaces , 1990, J. Complex..

[17]  M. Kearns,et al.  Crytographic limitations on learning Boolean formulae and finite automata , 1989, STOC '89.

[18]  Mihalis Yannakakis,et al.  Optimization, approximation, and complexity classes , 1991, STOC '88.

[19]  Ronald L. Rivest,et al.  Training a 3-node neural network is NP-complete , 1988, COLT '88.

[20]  Nimrod Megiddo,et al.  On the complexity of polyhedral separability , 1988, Discret. Comput. Geom..

[21]  Silvio Micali,et al.  How to construct random functions , 1986, JACM.

[22]  Franco P. Preparata,et al.  The Densest Hemisphere Problem , 1978, Theor. Comput. Sci..