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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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Citations
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Items 201–219 of 219
Mabu, S., Obayashi, M., & Kuremoto, T. (2015) · Applied Soft Computing · doi:10.1016/j.asoc.2015.07.020 · Cited by 48
Kim, J., Nakamura, T., Kikuchi, H., Yoshiuchi, K., Sasaki, T., & Yamamoto, Y. (2015) · IEEE Journal of Biomedical and Health Informatics · doi:10.1109/jbhi.2015.2440764 · Cited by 45
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Feature‐based fault diagnosis system of induction motors using vibration signal
Han, T., Yang, B., & Yin, Z. (2007) · Journal of Quality in Maintenance Engineering · doi:10.1108/13552510710753069 · Cited by 37
Application of Artificial Neural Networks Techniques to Computer Worm Detection
Stopel, D., Boger, Z., Moskovitch, R., Shahar, Y., & Elovici, Y. (2006) · The 2006 IEEE International Joint Conference on Neural Network Proceedings · doi:10.1109/ijcnn.2006.247059 · Cited by 35
El Khayat, I., Geurts, P., & Leduc, G. (2010) · Wireless Networks · doi:10.1007/s11276-008-0129-y · Cited by 34
Forewarning of hypotensive events using a Bayesian artificial neural network in neurocritical care
the BrainIT Group, Donald, R., Howells, T., Piper, I., Enblad, P., Nilsson, P., Chambers, I., Gregson, B., Citerio, G., Kiening, K., et al. (2019) · Journal of Clinical Monitoring and Computing · doi:10.1007/s10877-018-0139-y · Cited by 32
Zhang, F., Yang, F., Li, C., & Yuan, G. (2019) · IEEE Access · doi:10.1109/access.2019.2919103 · Cited by 29
A review of computational intelligence techniques in coral reef-related applications
Salcedo-Sanz, S., Cuadra, L., & Vermeij, M. J. A. (2016) · Ecological Informatics · doi:10.1016/j.ecoinf.2016.01.008 · Cited by 27
Age detection through keystroke dynamics from user authentication failures
Tsimperidis, I., Rostami, S., & Katos, V. (2017) · International Journal of Digital Crime and Forensics · doi:10.4018/ijdcf.2017010101 · Cited by 26
Cancer survival classification using integrated data sets and intermediate information
Kim, S., Park, T., & Kon, M. (2014) · Artificial Intelligence in Medicine · doi:10.1016/j.artmed.2014.06.003 · Cited by 25
MacDonald, M. E., Williams, R. J., Rajashekar, D., Stafford, R. B., Hanganu, A., Sun, H., Berman, A. J. L., McCreary, C. R., Frayne, R., Forkert, N. D., et al. (2020) · Neurobiology of Aging · doi:10.1016/j.neurobiolaging.2020.06.019 · Cited by 21
Cognitive Call Admission Control for VoIP over IEEE 802.11 Using Bayesian Networks
Quer, G., Baldo, N., & Zorzi, M. (2011) · 2011 IEEE Global Telecommunications Conference - GLOBECOM 2011 · doi:10.1109/glocom.2011.6133955 · Cited by 10
User-driven Call Admission Control for VoIP over WLAN with a Neural Network based cognitive engine
Baldo, N., Dini, P., & Nin-Guerrero, J. (2010) · 2010 2nd International Workshop on Cognitive Information Processing · doi:10.1109/cip.2010.5604128 · Cited by 10
Machine-learnt versus analytical models of TCP throughput
El Khayat, I., Geurts, P., & Leduc, G. (2007) · Computer Networks · doi:10.1016/j.comnet.2006.11.017 · Cited by 10
Machine Learning Based Analysis of Structural MRI for Epilepsy Diagnosis
Sahebzamani, G., Saffar, M., & Soltanian-Zadeh, H. (2019) · 2019 4th International Conference on Pattern Recognition and Image Analysis (IPRIA) · doi:10.1109/pria.2019.8785985 · Cited by 8
Comparison of Different Classification Methods for Breast Cancer Subtypes Prediction
Xu, J., Wu, P., Chen, Y., & Zhang, L. (2018) · 2018 International Conference on Security, Pattern Analysis, and Cybernetics (SPAC) · doi:10.1109/spac46244.2018.8965553 · Cited by 5
Intelligent Systems Engineering: Optical network applications
Leeson, M. S., Green, R. J., Higgins, M. D., & Hines, E. L. (2011) · 2011 13th International Conference on Transparent Optical Networks · doi:10.1109/icton.2011.5970868 · Cited by 3
Detection of articulation disorders using Empirical Mode Decomposition and neural networks
Georgoulas, G., Georgopoulos, V. C., Stylios, G. D., & Stylios, C. D. (2009) · 2009 International Joint Conference on Neural Networks · doi:10.1109/ijcnn.2009.5178935 · Cited by 3
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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Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
