54 lines
1.2 KiB
Text
54 lines
1.2 KiB
Text
**Relevant content viewed in the document**:
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## References
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**Relevant content viewed in the document**:
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from:
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**N.J., Smelter, & P.B., Baltes (Eds.) (2001).**
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**Encyclopedia of the Social and Behavioral Sciences.**
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**London: Elsevier Science.**
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**Article Title: Linear Algebra for Neural Networks**
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**By: Herve Abdi**
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**Author Address:** Herve Abdi, School of Human Development, MS: Gr.4.1, The University of Texas at Dallas, Richardson, TX 750833-0688, USA
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**Phone:** 972 883 2065, **fax:** 972 883 2491 **Date:** June 1, 2001
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**E-mail:** herve@utdallas.edu
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**Abstract**
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$$o=f\left(a\right)\enspace. \tag{6}$$
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For example, in _backpropagation networks_, the (nonlinear) transfer function is usually the logistic function
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$$o=f\left(a\right)=\operatorname{logist}\boldsymbol{w}^{\mskip-1.5mu \mathsf{T}} \boldsymbol{x}=\frac{1}{1+\exp\{-a\}}\enspace. \tag{7}$$
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-----------------------------------
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**Relevant content viewed in the document**:
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$\boldsymbol{x}$, and $\boldsymbol{w}$, the activation of the output cell is obtained as
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