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An introduction to computing with neural nets
Abstract
Artificial neural net models have been studied for many years in the hope of achieving human-like performance in the fields of speech and image recognition. These models are composed of many nonlinear computational elements operating in parallel and arranged in patterns reminiscent of biological neural nets. Computational elements or nodes are connected via weights that are typically adapted during use to improve performance. There has been a recent resurgence in the field of artificial neural nets caused by new net topologies and algorithms, analog VLSI implementation techniques, and the belief that massive parallelism is essential for high performance speech and image recognition. This paper provides an introduction to the field of artificial neural nets by reviewing six important neural net models that can be used for pattern classification. These nets are highly parallel building blocks that illustrate neural net components and design principles and can be used to construct more complex systems. In addition to describing these nets, a major emphasis is placed on exploring how some existing classification and clustering algorithms can be performed using simple neuron-like components. Single-layer nets can implement algorithms required by Gaussian maximum-likelihood classifiers and optimum minimum-error classifiers for binary patterns corrupted by noise. More generally, the decision regions required by any classification algorithm can be generated in a straightforward manner by three-layer feed-forward nets.
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Citations
Cites
Neural networks and physical systems with emergent collective computational abilities.
Hopfield, J. J. (1982) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.79.8.2554 · Cited by 19,270
A logical calculus of the ideas immanent in nervous activity
McCulloch, W. S., & Pitts, W. (1943) · The Bulletin of Mathematical Biophysics · doi:10.1007/bf02478259 · Cited by 18,566
Hebb, D. O. (2005) · doi:10.4324/9781410612403 · Cited by 9,825
Hopfield, J. J. (1984) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.81.10.3088 · Cited by 6,918
WIDROW, B., & HOFF, M. E. (1960) · doi:10.21236/ad0241531 · Cited by 4,009
Cited by
Approximation by superpositions of a sigmoidal function
Cybenko, G. (1989) · Mathematics of Control, Signals, and Systems · doi:10.1007/bf02551274 · Cited by 13,883
Artificial neural networks: a tutorial
Jain, A. K., Mao, J., & Mohiuddin, K. M. (1996) · Computer · doi:10.1109/2.485891 · Cited by 4,946
Hansen, L. K., & Salamon, P. (1990) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/34.58871 · Cited by 4,260
Forecasting with artificial neural networks:
Zhang, G., Eddy Patuwo, B., & Y. Hu, M. (1998) · International Journal of Forecasting · doi:10.1016/s0169-2070(97)00044-7 · Cited by 4,229
On the approximate realization of continuous mappings by neural networks
Funahashi, K.-I. (1989) · Neural Networks · doi:10.1016/0893-6080(89)90003-8 · Cited by 4,206
Neural Networks and the Bias/Variance Dilemma
Geman, S., Bienenstock, E., & Doursat, R. (1992) · Neural Computation · doi:10.1162/neco.1992.4.1.1 · Cited by 3,578
Phoneme recognition using time-delay neural networks
Waibel, A., Hanazawa, T., Hinton, G., Shikano, K., & Lang, K. J. (1989) · IEEE Transactions on Acoustics, Speech, and Signal Processing · doi:10.1109/29.21701 · Cited by 2,643
An introduction to multisensor data fusion
Hall, D. L., & Llinas, J. (1997) · Proceedings of the IEEE · doi:10.1109/5.554205 · Cited by 2,629
Statistical and machine learning forecasting methods: Concerns and ways forward
Makridakis, S., Spiliotis, E., & Assimakopoulos, V. (2018) · PLOS ONE · doi:10.1371/journal.pone.0194889 · Cited by 1,528
Neural networks and principal component analysis: Learning from examples without local minima
Baldi, P., & Hornik, K. (1989) · Neural Networks · doi:10.1016/0893-6080(89)90014-2 · Cited by 1,383
Machine learning in wireless sensor networks: Algorithms, strategies, and applications
Alsheikh, M. A., Lin, S., Niyato, D., & Tan, H.-P. (2014) · IEEE Communications Surveys & Tutorials · doi:10.1109/comst.2014.2320099 · Cited by 965
Fadlullah, Z. M., Tang, F., Mao, B., Kato, N., Akashi, O., Inoue, T., & Mizutani, K. (2017) · IEEE Communications Surveys & Tutorials · doi:10.1109/comst.2017.2707140 · Cited by 810
Artificial neural networks in bankruptcy prediction: General framework and cross-validation analysis
Zhang, G., Y. Hu, M., Eddy Patuwo, B., & C. Indro, D. (1999) · European Journal of Operational Research · doi:10.1016/s0377-2217(98)00051-4 · Cited by 677
Speech recognition in noisy environments: A survey
Gong, Y. (1995) · Speech Communication · doi:10.1016/0167-6393(94)00059-j · Cited by 635
Machine learning and AI in marketing–Connecting computing power to human insights
Ma, L., & Sun, B. (2020) · International Journal of Research in Marketing · doi:10.1016/j.ijresmar.2020.04.005 · Cited by 602
Godino-Llorente, J. I., Gomez-Vilda, P., & Blanco-Velasco, M. (2006) · IEEE Transactions on Biomedical Engineering · doi:10.1109/tbme.2006.871883 · Cited by 322
Solar Irradiance Forecasting Using Deep Neural Networks
Alzahrani, A., Shamsi, P., Dagli, C., & Ferdowsi, M. (2017) · Procedia Computer Science · doi:10.1016/j.procs.2017.09.045 · Cited by 280
On the computational power of neural nets
Siegelmann, H. T., & Sontag, E. D. (1992) · Proceedings of the fifth annual workshop on Computational learning theory · doi:10.1145/130385.130432 · Cited by 238
On‐line prediction of fermentation variables using neural networks
Thibault, J., Van Breusegem, V., & Chéruy, A. (1990) · Biotechnology and Bioengineering · doi:10.1002/bit.260361009 · Cited by 220
Blasco, J. A., Fueyo, N., Dopazo, C., & Ballester, J. (1998) · Combustion and Flame · doi:10.1016/s0010-2180(97)00211-3 · Cited by 169
Rapid eye movement sleep promotes cortical plasticity in the developing brain
Dumoulin Bridi, M. C., Aton, S. J., Seibt, J., Renouard, L., Coleman, T., & Frank, M. G. (2015) · Science Advances · doi:10.1126/sciadv.1500105 · Cited by 157
Christensen, J. C., Estepp, J. R., Wilson, G. F., & Russell, C. A. (2012) · NeuroImage · doi:10.1016/j.neuroimage.2011.07.091 · Cited by 144
An analog neural network processor with programmable topology
Boser, B. E., Sackinger, E., Bromley, J., Le Cun, Y., & Jackel, L. D. (1991) · IEEE Journal of Solid-State Circuits · doi:10.1109/4.104196 · Cited by 142
Chen, W. H., Shih, J. Y., & Wu, S. (2006) · International Journal of Electronic Finance · doi:10.1504/ijef.2006.008837 · Cited by 139
Neural network models for software development effort estimation: a comparative study
Nassif, A. B., Azzeh, M., Capretz, L. F., & Ho, D. (2016) · Neural Computing and Applications · doi:10.1007/s00521-015-2127-1 · Cited by 122
Kim, J. W., Weistroffer, H. R., & Redmond, R. T. (1993) · Expert Systems · doi:10.1111/j.1468-0394.1993.tb00093.x · Cited by 77
Tool-wear prediction using artificial neural networks
Ezugwu, E. O., Arthur, S. J., & Hines, E. L. (1995) · Journal of Materials Processing Technology · doi:10.1016/0924-0136(94)01351-z · Cited by 76
Genetic algorithms for generation of class boundaries
Pal, S. K., Bandyopadhyay, S., & Murthy, C. A. (1998) · IEEE Transactions on Systems, Man and Cybernetics, Part B (Cybernetics) · doi:10.1109/3477.735391 · Cited by 75
Son, H., Kim, C., Hwang, N., Kim, C., & Kang, Y. (2014) · Advanced Engineering Informatics · doi:10.1016/j.aei.2013.10.001 · Cited by 74
Santos-Garcı́a, G., Varela, G., Novoa, N., & Jiménez, M. F. (2004) · Artificial Intelligence in Medicine · doi:10.1016/s0933-3657(03)00059-9 · Cited by 70
An automated confirmatory system for analysis of mammograms
Peng, W., Mayorga, R. V., & Hussein, E. M. A. (2016) · Computer Methods and Programs in Biomedicine · doi:10.1016/j.cmpb.2015.09.019 · Cited by 43
Improving Health Care Organizational Management Through Neural Network Learning
Goss, E. P., & Vozikis, G. S. (2002) · Health Care Management Science · doi:10.1023/a:1019760901191 · Cited by 20
Structural Control: Past, Present, and Future
(1997) · doi:10.1061/(asce)0733-9399(1997)123:9(897
Metadata
- Authors
- Lippmann, R.
- Venue
- IEEE ASSP Magazine
- Published
- 1987
- Volume
- 4
- Issue
- 2
- Pages
- 4-22
- ISSN
- 0740-7467 (ISSN-L)
- OpenAlex
- W2101927907
- Cited by
- 7,825
- References
- 5
- FWCI
- 164.97
- Type
- journal article
- Language
- en
- Open access
- closed
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