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Neural networks for pattern recognition
Abstract
From the Publisher: This is the first comprehensive treatment of feed-forward neural networks from the perspective of statistical pattern recognition. After introducing the basic concepts, the book examines techniques for modelling probability density functions and the properties and merits of the multi-layer perceptron and radial basis function network models. Also covered are various forms of error functions, principal algorithms for error function minimalization, learning and generalization in neural networks, and Bayesian techniques and their applications. Designed as a text, with over 100 exercises, this fully up-to-date work will benefit anyone involved in the fields of neural computation and pattern recognition.
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(2012) · Choice Reviews Online · doi:10.5860/choice.49-3305 · Cited by 28,531
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Rasmussen, C. E., & Williams, C. K. I. (2005) · doi:10.7551/mitpress/3206.001.0001 · Cited by 11,705
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The graph neural network model
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(2012) · Cited by 8,009
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Graves, A., Fernández, S., Gomez, F., & Schmidhuber, J. (2006) · Proceedings of the 23rd international conference on Machine learning - ICML '06 · doi:10.1145/1143844.1143891 · Cited by 5,370
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Grimmer, J., & Stewart, B. M. (2013) · Political Analysis · doi:10.1093/pan/mps028 · Cited by 3,297
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Graves, A. (2012) · Studies in Computational Intelligence · doi:10.1007/978-3-642-24797-2 · Cited by 3,120
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Lotte, F., Congedo, M., Lécuyer, A., Lamarche, F., & Arnaldi, B. (2007) · Journal of Neural Engineering · doi:10.1088/1741-2560/4/2/r01 · Cited by 2,665
Khan, J., Wei, J. S., Ringnér, M., Saal, L. H., Ladanyi, M., Westermann, F., Berthold, F., Schwab, M., Antonescu, C. R., Peterson, C., et al. (2001) · Nature Medicine · doi:10.1038/89044 · Cited by 2,644
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Schroeder, A., Mueller, O., Stocker, S., Salowsky, R., Leiber, M., Gassmann, M., Lightfoot, S., Menzel, W., Granzow, M., & Ragg, T. (2006) · BMC Molecular Biology · doi:10.1186/1471-2199-7-3 · Cited by 2,623
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Gu, S., Kelly, B., & Xiu, D. (2020) · The Review of Financial Studies · doi:10.1093/rfs/hhaa009 · Cited by 2,545
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On the mathematical foundations of learning
Cucker, F., & Smale, S. (2001) · Bulletin of the American Mathematical Society · doi:10.1090/s0273-0979-01-00923-5 · Cited by 1,545
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Everingham, M., Zisserman, A., Williams, C. K. I., Van Gool, L., Allan, M., Bishop, C. M., Chapelle, O., Dalal, N., Deselaers, T., Dorkó, G., et al. (2006) · Lecture Notes in Computer Science · doi:10.1007/11736790_8 · Cited by 765
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Jo, T., Nho, K., & Saykin, A. J. (2019) · Frontiers in Aging Neuroscience · doi:10.3389/fnagi.2019.00220 · Cited by 724
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Leeb, R., Lee, F., Keinrath, C., Scherer, R., Bischof, H., & Pfurtscheller, G. (2007) · IEEE Transactions on Neural Systems and Rehabilitation Engineering · doi:10.1109/tnsre.2007.906956 · Cited by 513
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Reagen, B., Whatmough, P., Adolf, R., Rama, S., Lee, H., Lee, S. K., Hernández-Lobato, J. M., Wei, G.-Y., & Brooks, D. (2016) · ACM SIGARCH Computer Architecture News · doi:10.1145/3007787.3001165 · Cited by 446
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Metadata
- Venue
- Choice Reviews Online
- Publisher
- American Library Association
- Published
- 1994-06-01
- Volume
- 31
- Issue
- 10
- Pages
- 31-5500-31-5500
- ISSN
- 0009-4978 (ISSN-L)
- 1523-8253
- OpenAlex
- W1554663460
- Cited by
- 18,538
- References
- 0
- Type
- book review
- Language
- en
- Open access
- closed
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