logistic (softmax) choice function (SoftMax Inc)
90
Structured Review
SoftMax Inc
logistic (softmax) choice function
Logistic (Softmax) Choice Function, supplied by SoftMax Inc, used in various techniques. Bioz Stars score: 90/100, based on 1 PubMed citations. ZERO BIAS - scores, article reviews, protocol conditions and more
https://www.bioz.com/product/activation+function+(sigmoidal+function)/sigmoid++softmax++function/pmc05764197-420-7-7
Average 90 stars, based on 1 article reviews
Logistic (Softmax) Choice Function, supplied by SoftMax Inc, used in various techniques. Bioz Stars score: 90/100, based on 1 PubMed citations. ZERO BIAS - scores, article reviews, protocol conditions and more
https://www.bioz.com/product/activation+function+(sigmoidal+function)/sigmoid++softmax++function/pmc05764197-420-7-7
Average 90 stars, based on 1 article reviews
logistic (softmax) choice function - by Bioz Stars,
2026-09
90/100 stars
Images
Related Articles
Activation Assay:Article Title: Neural Decoding of EEG Signals with Machine Learning: A Systematic Review Article Snippet: [ ] , 2016 , AD , Own database , WT , MC-DCNN Sigmoid and Article Title: Prefrontal mechanisms combining rewards and beliefs in human decision-making Article Snippet: Following your suggestion, we replaced equations lines 671, 745 and 757 in the original manuscript with single equations using the standard Article Title: Adversarially-Regularized Mixed Effects Deep Learning (ARMED) Models Improve Interpretability, Performance, and Generalization on Clustered (non- iid ) Data Article Snippet: Conventional autoencoder Adversarial classifier Random effects convolution block Strided conv, batch norm, Article Title: A Quantitative Comparison of Overlapping and Non-Overlapping Sliding Windows for Human Activity Recognition Using Inertial Sensors Article Snippet: The Article Title: Aberrant computational mechanisms of social learning and decision-making in schizophrenia and borderline personality disorder Article Snippet: In the response model, we used the combined belief b ( t ) ( ) in a logistic Article Title: Circuit mechanisms for the chemical modulation of cortex-wide network interactions and behavioral variability Article Snippet: The LFI on trial ( t ) LFI hor was defined as LFI hor ( t ) = LI hor ( t ) LI hor ( t ) + LI ver ( t ) (19) LFI hor was transformed into the choice probability p ( c t = hor), defined with respect to the Article Title: Adversarially-Regularized Mixed Effects Deep Learning (ARMED) Models Improve Interpretability, Performance, and Generalization on Clustered (non- iid ) Data Article Snippet: Article Title: Decision-Making with Predictions of Others' Likely and Unlikely Choices in the Human Brain Article Snippet: 250 words; Introduction 611 words; Discussion 1499 words 58 |
![k GPRELM’s overfitting (for a fixed <t>\documentclass[12pt]{minimal}</t> \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\lambda =1$$\end{document} λ = 1 )](https://pub-med-central-images-cdn.bioz.com/pub_med_central_ids_ending_with_8478/pmc09838478/pmc09838478__10115_2022_1803_Fig2_HTML.jpg)
![Predictive performances of k GPRELM ( \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\big ( {L,\sigma _N ,\lambda ^2 }\big )=\big ({190,2^{- 20},2^{-9} }\big )$$\end{document} ( L , σ N , λ 2 ) = ( 190 , 2 - 20 , 2 - 9 ) ) and c GPRELM ( \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\big ( {L,\sigma _N}\big )=\big ({80,2^{- 20}}\big )$$\end{document} ( L , σ N ) = ( 80 , 2 - 20 ) ) on 200 SinC instances](https://pub-med-central-images-cdn.bioz.com/pub_med_central_ids_ending_with_8478/pmc09838478/pmc09838478__10115_2022_1803_Fig5_HTML.jpg)
![Ranks of \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\mathrm{{K}}\left( {\mathrm{{H}},\mathrm{{H}}} \right) + \sigma _N^2 \mathrm{{I}}$$\end{document} K H , H + σ N 2 I and \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\mathrm{{C}}\left( {\mathrm{{H}},\mathrm{{H}}} \right) + \sigma _N^2 \mathrm{{I}}$$\end{document} C H , H + σ N 2 I on two representative classification data sets](https://pub-med-central-images-cdn.bioz.com/pub_med_central_ids_ending_with_8478/pmc09838478/pmc09838478__10115_2022_1803_Fig6_HTML.jpg)
![Ranks of \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\mathrm{{K}}\left( {\mathrm{{H}},\mathrm{{H}}} \right) + \sigma _N^2 \mathrm{{I}}$$\end{document} K H , H + σ N 2 I and \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\mathrm{{C}}\left( {\mathrm{{H}},\mathrm{{H}}} \right) + \sigma _N^2 \mathrm{{I}}$$\end{document} C H , H + σ N 2 I on two representative regression data sets](https://pub-med-central-images-cdn.bioz.com/pub_med_central_ids_ending_with_8478/pmc09838478/pmc09838478__10115_2022_1803_Fig7_HTML.jpg)