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Volumn 165, Issue , 2007, Pages 521-534

On the challenge of learning complex functions

Author keywords

artificial intelligence; deep belief networks; deep neural networks; kernel machines; learning abstractions; multi layer neural networks; template matching; theory of learning algorithms

Indexed keywords

ALGORITHM; ARCHITECTURE; COMPLEX FORMATION; INTELLIGENCE; KERNEL METHOD; LEARNING; METHODOLOGY; NERVE CELL NETWORK; NORMAL DISTRIBUTION; PRIORITY JOURNAL; REVIEW;

EID: 34848884796     PISSN: 00796123     EISSN: None     Source Type: Book Series    
DOI: 10.1016/S0079-6123(06)65033-4     Document Type: Review
Times cited : (14)

References (35)
  • 1
    • 0002970176 scopus 로고
    • 1 - formulae on finite structures
    • 1 - formulae on finite structures. Ann. Pure Appl. Logic 24 1 (1983) 48
    • (1983) Ann. Pure Appl. Logic , vol.24 , Issue.1 , pp. 48
    • Ajtai, M.1
  • 2
    • 84947904777 scopus 로고    scopus 로고
    • Allender, E. (1996) Circuit complexity before the dawn of the new millennium. In: 16th Annual Conference on Foundations of Software Technology and Theoretical Computer Science, Hyderbad, India, pp. 1-18. Lecture Notes in Computer Science 1180.
  • 3
    • 77954662106 scopus 로고    scopus 로고
    • The curse of highly variable functions for local kernel machines
    • Weiss Y., Schölkopf B., and Platt J. (Eds), MIT Press, Cambridge, MA
    • Bengio Y., Delalleau O., and Le Roux N. The curse of highly variable functions for local kernel machines. In: Weiss Y., Schölkopf B., and Platt J. (Eds). Advances in Neural Information Processing Systems 18 (2006), MIT Press, Cambridge, MA 107-114
    • (2006) Advances in Neural Information Processing Systems 18 , pp. 107-114
    • Bengio, Y.1    Delalleau, O.2    Le Roux, N.3
  • 4
    • 4544291266 scopus 로고
    • Diffusion of context and credit information in Markovian models
    • Bengio Y., and Frasconi P. Diffusion of context and credit information in Markovian models. J. Artif. Intell. Res. 3 (1995) 223-244
    • (1995) J. Artif. Intell. Res. , vol.3 , pp. 223-244
    • Bengio, Y.1    Frasconi, P.2
  • 6
    • 34547975052 scopus 로고    scopus 로고
    • Scaling learning algorithms towards AI
    • Bottou L., Chapelle O., DeCoste D., and Weston J. (Eds), MIT Press, Cambridge, MA
    • Bengio Y., and Le Cun Y. Scaling learning algorithms towards AI. In: Bottou L., Chapelle O., DeCoste D., and Weston J. (Eds). Large Scale Kernel Machines (2007), MIT Press, Cambridge, MA
    • (2007) Large Scale Kernel Machines
    • Bengio, Y.1    Le Cun, Y.2
  • 8
    • 0028392483 scopus 로고
    • Learning long-term dependencies with gradient descent is difficult
    • Bengio Y., Simard P., and Frasconi P. Learning long-term dependencies with gradient descent is difficult. IEEE Trans. Neural Netw. 5 2 (1994) 157-166
    • (1994) IEEE Trans. Neural Netw. , vol.5 , Issue.2 , pp. 157-166
    • Bengio, Y.1    Simard, P.2    Frasconi, P.3
  • 10
    • 0026966646 scopus 로고    scopus 로고
    • Boser, B., Guyon, I. and Vapnik, V. (1992) A training algorithm for optimal margin classifiers. In: Fifth Annual Workshop on Computational Learning Theory, Pittsburgh, pp. 144-152 .
  • 11
    • 0001341901 scopus 로고
    • Active learning with statistical models
    • Tesauro G., Touretzky D., and Leen T. (Eds), MIT Press, Cambridge, MA
    • Cohn D., Ghahramani Z., and Jordan M.I. Active learning with statistical models. In: Tesauro G., Touretzky D., and Leen T. (Eds). Advances in Neural Information Processing Systems 7 (1995), MIT Press, Cambridge, MA
    • (1995) Advances in Neural Information Processing Systems 7
    • Cohn, D.1    Ghahramani, Z.2    Jordan, M.I.3
  • 12
    • 34249753618 scopus 로고
    • Support vector networks
    • Cortes C., and Vapnik V. Support vector networks. Machine Learn. 20 (1995) 273-297
    • (1995) Machine Learn. , vol.20 , pp. 273-297
    • Cortes, C.1    Vapnik, V.2
  • 14
    • 85057230110 scopus 로고    scopus 로고
    • Hierarchical recurrent neural networks for long-term dependencies
    • Touretzky D., Mozer M., and Hasselmo M. (Eds), MIT Press, Cambridge, MA
    • ElHihi S., and Bengio Y. Hierarchical recurrent neural networks for long-term dependencies. In: Touretzky D., Mozer M., and Hasselmo M. (Eds). Advances in Neural Information Processing Systems 8 (1996), MIT Press, Cambridge, MA 493-499
    • (1996) Advances in Neural Information Processing Systems 8 , pp. 493-499
    • ElHihi, S.1    Bengio, Y.2
  • 15
    • 0031211090 scopus 로고    scopus 로고
    • A decision theoretic generalization of on-line learning and an application to boosting
    • Freund Y., and Schapire R.E. A decision theoretic generalization of on-line learning and an application to boosting. J. Comput. Syst. Sci. 55 1 (1997) 119-139
    • (1997) J. Comput. Syst. Sci. , vol.55 , Issue.1 , pp. 119-139
    • Freund, Y.1    Schapire, R.E.2
  • 16
    • 0000413255 scopus 로고    scopus 로고
    • Active learning in multilayer perceptrons
    • Touretzky D., Mozer M., and Hasselmo M. (Eds), MIT Press, Cambridge, MA
    • Fukumizu K. Active learning in multilayer perceptrons. In: Touretzky D., Mozer M., and Hasselmo M. (Eds). Advances in Neural Information Processing Systems 8 (1996), MIT Press, Cambridge, MA
    • (1996) Advances in Neural Information Processing Systems 8
    • Fukumizu, K.1
  • 17
    • 24944573087 scopus 로고    scopus 로고
    • Is postnatal neocortical maturation hierarchical?
    • Guillery R. Is postnatal neocortical maturation hierarchical?. Trends Neurosci. 28 10 (2005) 512-517
    • (2005) Trends Neurosci. , vol.28 , Issue.10 , pp. 512-517
    • Guillery, R.1
  • 20
    • 33746600649 scopus 로고    scopus 로고
    • Reducing the dimensionality of data with neural networks
    • Hinton G., and Salakhutdinov R. Reducing the dimensionality of data with neural networks. Science 313 5786 (2006) 504-507
    • (2006) Science , vol.313 , Issue.5786 , pp. 504-507
    • Hinton, G.1    Salakhutdinov, R.2
  • 21
    • 33745805403 scopus 로고    scopus 로고
    • A fast learning algorithm for deep belief nets
    • Hinton G.E., Osindero S., and Teh Y. A fast learning algorithm for deep belief nets. Neural Comput. 18 (2006) 1527-1554
    • (2006) Neural Comput. , vol.18 , pp. 1527-1554
    • Hinton, G.E.1    Osindero, S.2    Teh, Y.3
  • 24
    • 0032203257 scopus 로고    scopus 로고
    • Gradient based learning applied to document recognition
    • LeCun Y., Bottou L., Bengio Y., and Haffner P. Gradient based learning applied to document recognition. Proc. IEEE 86 11 (1998) 2278-2324
    • (1998) Proc. IEEE , vol.86 , Issue.11 , pp. 2278-2324
    • LeCun, Y.1    Bottou, L.2    Bengio, Y.3    Haffner, P.4
  • 27
    • 84864069017 scopus 로고    scopus 로고
    • Efficient learning of sparse representations with an energy-based model
    • Scholkopf B., Platt J., and Hoffman T. (Eds), MIT Press, Cambridge, MA
    • Ranzato M., Poultney C., Chopra S., and LeCun Y. Efficient learning of sparse representations with an energy-based model. In: Scholkopf B., Platt J., and Hoffman T. (Eds). Advances in Neural Information Processing Systems (NIPS 2006) (2006), MIT Press, Cambridge, MA
    • (2006) Advances in Neural Information Processing Systems (NIPS 2006)
    • Ranzato, M.1    Poultney, C.2    Chopra, S.3    LeCun, Y.4
  • 28
    • 34848850539 scopus 로고    scopus 로고
    • Rosenblatt, F. (1957) The perceptron: a perceiving and recognizing automaton. Tech. rep. 85-460-1, Cornell Aeronautical Laboratory, Ithaca, NY.
  • 29
    • 0022471098 scopus 로고
    • Learning representations by backpropagating errors
    • Rumelhart D., Hinton G., and Williams R. Learning representations by backpropagating errors. Nature 323 (1986) 533-536
    • (1986) Nature , vol.323 , pp. 533-536
    • Rumelhart, D.1    Hinton, G.2    Williams, R.3
  • 30
    • 0040081674 scopus 로고    scopus 로고
    • Descartes' rule of signs for radial basis function neural networks
    • Schmitt M. Descartes' rule of signs for radial basis function neural networks. Neural Comput. 14 12 (2002) 2997-3011
    • (2002) Neural Comput. , vol.14 , Issue.12 , pp. 2997-3011
    • Schmitt, M.1
  • 32
    • 0001046225 scopus 로고
    • Practical issues in temporal difference learning
    • Tesauro G. Practical issues in temporal difference learning. Machine Learn. 8 (1992) 257-277
    • (1992) Machine Learn. , vol.8 , pp. 257-277
    • Tesauro, G.1
  • 33
    • 0036782663 scopus 로고    scopus 로고
    • Many-layered learning
    • Utgoff P., and Stracuzzi D. Many-layered learning. Neural Comput. 14 (2002) 2497-2539
    • (2002) Neural Comput. , vol.14 , pp. 2497-2539
    • Utgoff, P.1    Stracuzzi, D.2
  • 34
    • 0003991806 scopus 로고    scopus 로고
    • Wiley Lecture Notes in Economics and Mathematical Systems
    • Vapnik V. Statistical Learning Theory Vol. 454 (1998), Wiley Lecture Notes in Economics and Mathematical Systems
    • (1998) Statistical Learning Theory , vol.454
    • Vapnik, V.1
  • 35
    • 0000459353 scopus 로고    scopus 로고
    • The lack of a priori distinction between learning algorithms
    • Wolpert D. The lack of a priori distinction between learning algorithms. Neural Comput. 8 7 (1996) 1341-1390
    • (1996) Neural Comput. , vol.8 , Issue.7 , pp. 1341-1390
    • Wolpert, D.1


* 이 정보는 Elsevier사의 SCOPUS DB에서 KISTI가 분석하여 추출한 것입니다.