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  2. ISBN 13: 9780471135340
  3. Pattern Recognition
  4. Figure from Pattern Recognition Concepts Pattern Recognition Problems - Semantic Scholar
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Using this textbook dataset as an exemplar, we provide a preliminary guide to neural networking approaches to the analysis of behavioral outcomes. When employing conventional multivariate procedures only, the sample dataset demonstrated significant familywise error rates; however, these outcomes did not provide sufficient information for identifying the curvilinear patterns that existed within these records. When converted to natural logs and reanalyzed by the SOM, the exemplar dataset showed the actual best fit performance patterns exhibited by all members of the experimental and control groups.

The SOM and related neural network algorithms appear to have unique potential in the recognition of nonlinear but unified data patterns frequently exhibited within academic and social outcomes. In particular, the SOM allows the researcher to conduct a "finer grain" analysis identifying critically important similarities and differences that can inform treatment well beyond the probability values derived from conventional statistical techniques. Open Journal Systems. Journal Help. Cenci, C.

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Nagar, A. Chai, Huang, T. Chan, S. Lo, B.

Sahiner et al. Chandrasekaran, M. Palaniswami, T. Caelli, Range image segmentation by dynamic neural network architecture, Pattern Recognition 29 2 Chang, G. Han, J. Valverde et al. Chang, W. Kuo, D. Chen et al. Chen, On the relationships between statistical pattern recognition and artificial neural networks, International Journal of Pattern Recognition and Artificial Intelligence 5 4 Chen, E. Tsao, W. Chen, W.

Chen, M. Defrise, F. Cheng, J. Lin, C.


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  5. The Souls of Black Folk (Oxford Worlds Classics).
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Mingolla, Neural dynamics of motion grouping - from aperture ambiguity to object speed and direction [review], Journal of the Optical Society of America A-Optics and Image Science 14 10 Chiou, J. Chong, J. Jia, Assessments of neural network classifier output codings using variability of Hamming distance, Pattern Recognition Letters 17 8 Christensen, A. Andersen, T.

Christmas, J. Kittler, M. Petrou, Analytical approaches to the neural net architecture design, Proc.

ISBN 13: 9780471135340

Chua, L. Chung, C. Tsai, E. Cornfield, Statistical classification methods, Proc. Cottet, M. Courtney, L. Finkel, G.

Pattern Recognition

Cruz, G. Pajares, J. Aranda et al.

Aranda, A neural network model in stereovision matching, Neural Networks 8 5 Dassen, M. Egmont-Petersen, R. Vardas, ed. Duin, P.


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  • Verbeek et al. DeKruger, Hunt, B. Delopoulos, A. Tirakis, S. DeMers, G. Cottrell, Non-linear dimensionality reduction, Proc. Advances in Neural Information Processing Systems, , pp. Desachy, L. Roux, E. Zahzah, Numeric and symbolic data fusion: A soft computing approach to remote sensing images analysis, Pattern Recognition Letters 17 13 Sheng, P.

    Chevrette, Three-dimensional object recognition from two-dimensional images using wavelet transforms and neural networks, Optical Engineering 37 3 Devijver, J. Kittler, Pattern recognition: a statistical approach, Englewood Cliffs, London, DeVilliers, E. Dewaard, Neural techniques and postal code detection, Pattern Recognition Letters 15 2 Uebel, D. Dony, S. Dror, M.

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    Zagaeski, C. Moss, 3-D target recognition via sonar - a neural network model, Neural Networks 8 1 Du Buf, M. Kardan, M.

    Introduction to pattern recognition

    Spann, Texture feature performance for image segmentation, Pattern Recognition 23 Egmont-Petersen, J. Talmon, J. Brender et al. Talmon, A. Hasman, Robustness metrics for measuring the influence of additive noise on the performance of statistical classifiers, International Journal of Medical Informatics 46 2 Hasman et al.

    Egmont-Petersen, W. Dassen, C. Kirchhof et al. Computers in Cardiology , Cleveland, , pp. Egmont-Petersen, E.

    歡迎光臨Tamiman在痞客邦的小天地

    Pelikan, Detection of bone tumours in radiographs using neural networks, Pattern Analysis and Applications 2 2 Egmont-Petersen, T. Arts, Recognition of radiopaque markers in X-ray images using a neural network as nonlinear filter, Pattern Recognition Letters 20 5 Dassen, J.

    Reiber, Sequential selection of discrete features for neural networks - a Bayesian approach to building a cascade, Pattern Recognition Letters 20 Egmont-Petersen, U. Schreiner, S. Tromp et al. Einstein, J. Barba, P. Unger et al. Ercal, A. Chawla, W. Stoecker et al. Fang, B. Sheu, O. Figueiredo, J.