matlab function lasso Search Results


regularized linear regression implemented in the matlab lasso function  (MathWorks Inc)
93
MathWorks Inc regularized linear regression implemented in the matlab lasso function
Regularized Linear Regression Implemented In The Matlab Lasso Function, supplied by MathWorks Inc, used in various techniques. Bioz Stars score: 93/100, based on 1 PubMed citations. ZERO BIAS - scores, article reviews, protocol conditions and more
https://www.bioz.com/result/regularized linear regression implemented in the matlab lasso function/product/MathWorks Inc
Average 93 stars, based on 1 article reviews
regularized linear regression implemented in the matlab lasso function - by Bioz Stars, 2026-04
93/100 stars
  Buy from Supplier
matlab function lasso.m  (MathWorks Inc)
90
MathWorks Inc matlab function lasso.m
Matlab Function Lasso.M, supplied by MathWorks 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/result/matlab function lasso.m/product/MathWorks Inc
Average 90 stars, based on 1 article reviews
matlab function lasso.m - by Bioz Stars, 2026-04
90/100 stars
  Buy from Supplier
lasso function  (MathWorks Inc)
90
MathWorks Inc lasso function
Lasso Function, supplied by MathWorks 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/result/lasso function/product/MathWorks Inc
Average 90 stars, based on 1 article reviews
lasso function - by Bioz Stars, 2026-04
90/100 stars
  Buy from Supplier
linear lasso method lasso function  (MathWorks Inc)
90
MathWorks Inc linear lasso method lasso function
Linear Lasso Method Lasso Function, supplied by MathWorks 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/result/linear lasso method lasso function/product/MathWorks Inc
Average 90 stars, based on 1 article reviews
linear lasso method lasso function - by Bioz Stars, 2026-04
90/100 stars
  Buy from Supplier
lasso logistic regression classifier  (MathWorks Inc)
90
MathWorks Inc lasso logistic regression classifier
A Localizer Task. The Localizer Task was completed prior to the risky-decision task and to learning choice-outcome probabilities. On each trial participants were shown an outcome or choice stimulus, and, on the next screen, selected a word corresponding to the stimulus they had just observed. B Activation Probability measure. We trained <t>lasso-regularized</t> <t>logistic</t> <t>regression</t> classifiers to discriminate MEG data from when a given outcome stimulus was presented compared to data from presentation of all other images and inter-trial intervals. Each <t>classifier</t> output an estimated probability that its stimulus was being presented (Activation Probability). Separate classifiers were trained at successive 10 ms bins of MEG data around stimulus presentation. In the example, lines display the group-mean (+/− s.e.m.) activation probability measure for the classifier corresponding to O2, for each training timepoint, applied to held out data from the same corresponding test timepoint. Color designates the true outcome stimulus presented. C Decoding accuracy. Cross-validation accuracy is the proportion of trials for which the classifier corresponding to the presented outcome (for held-out data) had the highest activation probability. Lines denote mean accuracy (+/− s.e.m.) for each set of 10 ms time-binned outcome classifiers, applied to the same time-bin on held out examples. Dashed line designates permutation threshold corresponding to the 95 percentile peak threshold for accuracy lines generated with shuffled labels. D Temporal specificity. Classifiers trained on each 10 ms time bin were also tested on every time bin from −350 to 800 ms following presentation of stimuli from held out data. The resulting accuracy image demonstrates temporal selectivity. Classifiers identify with good accuracy representations of stimuli specific to the timepoint on which they were trained. B – D Values reflect group means across 19 participants. A Scissor image was created by scarlab and published on svgrepo.com under an MIT license.
Lasso Logistic Regression Classifier, supplied by MathWorks 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/result/lasso logistic regression classifier/product/MathWorks Inc
Average 90 stars, based on 1 article reviews
lasso logistic regression classifier - by Bioz Stars, 2026-04
90/100 stars
  Buy from Supplier
fitrlinear and lasso functions in matlab 2021a  (MathWorks Inc)
90
MathWorks Inc fitrlinear and lasso functions in matlab 2021a
A Localizer Task. The Localizer Task was completed prior to the risky-decision task and to learning choice-outcome probabilities. On each trial participants were shown an outcome or choice stimulus, and, on the next screen, selected a word corresponding to the stimulus they had just observed. B Activation Probability measure. We trained <t>lasso-regularized</t> <t>logistic</t> <t>regression</t> classifiers to discriminate MEG data from when a given outcome stimulus was presented compared to data from presentation of all other images and inter-trial intervals. Each <t>classifier</t> output an estimated probability that its stimulus was being presented (Activation Probability). Separate classifiers were trained at successive 10 ms bins of MEG data around stimulus presentation. In the example, lines display the group-mean (+/− s.e.m.) activation probability measure for the classifier corresponding to O2, for each training timepoint, applied to held out data from the same corresponding test timepoint. Color designates the true outcome stimulus presented. C Decoding accuracy. Cross-validation accuracy is the proportion of trials for which the classifier corresponding to the presented outcome (for held-out data) had the highest activation probability. Lines denote mean accuracy (+/− s.e.m.) for each set of 10 ms time-binned outcome classifiers, applied to the same time-bin on held out examples. Dashed line designates permutation threshold corresponding to the 95 percentile peak threshold for accuracy lines generated with shuffled labels. D Temporal specificity. Classifiers trained on each 10 ms time bin were also tested on every time bin from −350 to 800 ms following presentation of stimuli from held out data. The resulting accuracy image demonstrates temporal selectivity. Classifiers identify with good accuracy representations of stimuli specific to the timepoint on which they were trained. B – D Values reflect group means across 19 participants. A Scissor image was created by scarlab and published on svgrepo.com under an MIT license.
Fitrlinear And Lasso Functions In Matlab 2021a, supplied by MathWorks 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/result/fitrlinear and lasso functions in matlab 2021a/product/MathWorks Inc
Average 90 stars, based on 1 article reviews
fitrlinear and lasso functions in matlab 2021a - by Bioz Stars, 2026-04
90/100 stars
  Buy from Supplier
matlab function lasso  (MathWorks Inc)
90
MathWorks Inc matlab function lasso
A Localizer Task. The Localizer Task was completed prior to the risky-decision task and to learning choice-outcome probabilities. On each trial participants were shown an outcome or choice stimulus, and, on the next screen, selected a word corresponding to the stimulus they had just observed. B Activation Probability measure. We trained <t>lasso-regularized</t> <t>logistic</t> <t>regression</t> classifiers to discriminate MEG data from when a given outcome stimulus was presented compared to data from presentation of all other images and inter-trial intervals. Each <t>classifier</t> output an estimated probability that its stimulus was being presented (Activation Probability). Separate classifiers were trained at successive 10 ms bins of MEG data around stimulus presentation. In the example, lines display the group-mean (+/− s.e.m.) activation probability measure for the classifier corresponding to O2, for each training timepoint, applied to held out data from the same corresponding test timepoint. Color designates the true outcome stimulus presented. C Decoding accuracy. Cross-validation accuracy is the proportion of trials for which the classifier corresponding to the presented outcome (for held-out data) had the highest activation probability. Lines denote mean accuracy (+/− s.e.m.) for each set of 10 ms time-binned outcome classifiers, applied to the same time-bin on held out examples. Dashed line designates permutation threshold corresponding to the 95 percentile peak threshold for accuracy lines generated with shuffled labels. D Temporal specificity. Classifiers trained on each 10 ms time bin were also tested on every time bin from −350 to 800 ms following presentation of stimuli from held out data. The resulting accuracy image demonstrates temporal selectivity. Classifiers identify with good accuracy representations of stimuli specific to the timepoint on which they were trained. B – D Values reflect group means across 19 participants. A Scissor image was created by scarlab and published on svgrepo.com under an MIT license.
Matlab Function Lasso, supplied by MathWorks 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/result/matlab function lasso/product/MathWorks Inc
Average 90 stars, based on 1 article reviews
matlab function lasso - by Bioz Stars, 2026-04
90/100 stars
  Buy from Supplier
function lasso  (MathWorks Inc)
90
MathWorks Inc function lasso
A Localizer Task. The Localizer Task was completed prior to the risky-decision task and to learning choice-outcome probabilities. On each trial participants were shown an outcome or choice stimulus, and, on the next screen, selected a word corresponding to the stimulus they had just observed. B Activation Probability measure. We trained <t>lasso-regularized</t> <t>logistic</t> <t>regression</t> classifiers to discriminate MEG data from when a given outcome stimulus was presented compared to data from presentation of all other images and inter-trial intervals. Each <t>classifier</t> output an estimated probability that its stimulus was being presented (Activation Probability). Separate classifiers were trained at successive 10 ms bins of MEG data around stimulus presentation. In the example, lines display the group-mean (+/− s.e.m.) activation probability measure for the classifier corresponding to O2, for each training timepoint, applied to held out data from the same corresponding test timepoint. Color designates the true outcome stimulus presented. C Decoding accuracy. Cross-validation accuracy is the proportion of trials for which the classifier corresponding to the presented outcome (for held-out data) had the highest activation probability. Lines denote mean accuracy (+/− s.e.m.) for each set of 10 ms time-binned outcome classifiers, applied to the same time-bin on held out examples. Dashed line designates permutation threshold corresponding to the 95 percentile peak threshold for accuracy lines generated with shuffled labels. D Temporal specificity. Classifiers trained on each 10 ms time bin were also tested on every time bin from −350 to 800 ms following presentation of stimuli from held out data. The resulting accuracy image demonstrates temporal selectivity. Classifiers identify with good accuracy representations of stimuli specific to the timepoint on which they were trained. B – D Values reflect group means across 19 participants. A Scissor image was created by scarlab and published on svgrepo.com under an MIT license.
Function Lasso, supplied by MathWorks 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/result/function lasso/product/MathWorks Inc
Average 90 stars, based on 1 article reviews
function lasso - by Bioz Stars, 2026-04
90/100 stars
  Buy from Supplier
lasso.m library function  (MathWorks Inc)
90
MathWorks Inc lasso.m library function
A Localizer Task. The Localizer Task was completed prior to the risky-decision task and to learning choice-outcome probabilities. On each trial participants were shown an outcome or choice stimulus, and, on the next screen, selected a word corresponding to the stimulus they had just observed. B Activation Probability measure. We trained <t>lasso-regularized</t> <t>logistic</t> <t>regression</t> classifiers to discriminate MEG data from when a given outcome stimulus was presented compared to data from presentation of all other images and inter-trial intervals. Each <t>classifier</t> output an estimated probability that its stimulus was being presented (Activation Probability). Separate classifiers were trained at successive 10 ms bins of MEG data around stimulus presentation. In the example, lines display the group-mean (+/− s.e.m.) activation probability measure for the classifier corresponding to O2, for each training timepoint, applied to held out data from the same corresponding test timepoint. Color designates the true outcome stimulus presented. C Decoding accuracy. Cross-validation accuracy is the proportion of trials for which the classifier corresponding to the presented outcome (for held-out data) had the highest activation probability. Lines denote mean accuracy (+/− s.e.m.) for each set of 10 ms time-binned outcome classifiers, applied to the same time-bin on held out examples. Dashed line designates permutation threshold corresponding to the 95 percentile peak threshold for accuracy lines generated with shuffled labels. D Temporal specificity. Classifiers trained on each 10 ms time bin were also tested on every time bin from −350 to 800 ms following presentation of stimuli from held out data. The resulting accuracy image demonstrates temporal selectivity. Classifiers identify with good accuracy representations of stimuli specific to the timepoint on which they were trained. B – D Values reflect group means across 19 participants. A Scissor image was created by scarlab and published on svgrepo.com under an MIT license.
Lasso.M Library Function, supplied by MathWorks 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/result/lasso.m library function/product/MathWorks Inc
Average 90 stars, based on 1 article reviews
lasso.m library function - by Bioz Stars, 2026-04
90/100 stars
  Buy from Supplier
lasso regression  (MathWorks Inc)
96
MathWorks Inc lasso regression
A Localizer Task. The Localizer Task was completed prior to the risky-decision task and to learning choice-outcome probabilities. On each trial participants were shown an outcome or choice stimulus, and, on the next screen, selected a word corresponding to the stimulus they had just observed. B Activation Probability measure. We trained <t>lasso-regularized</t> <t>logistic</t> <t>regression</t> classifiers to discriminate MEG data from when a given outcome stimulus was presented compared to data from presentation of all other images and inter-trial intervals. Each <t>classifier</t> output an estimated probability that its stimulus was being presented (Activation Probability). Separate classifiers were trained at successive 10 ms bins of MEG data around stimulus presentation. In the example, lines display the group-mean (+/− s.e.m.) activation probability measure for the classifier corresponding to O2, for each training timepoint, applied to held out data from the same corresponding test timepoint. Color designates the true outcome stimulus presented. C Decoding accuracy. Cross-validation accuracy is the proportion of trials for which the classifier corresponding to the presented outcome (for held-out data) had the highest activation probability. Lines denote mean accuracy (+/− s.e.m.) for each set of 10 ms time-binned outcome classifiers, applied to the same time-bin on held out examples. Dashed line designates permutation threshold corresponding to the 95 percentile peak threshold for accuracy lines generated with shuffled labels. D Temporal specificity. Classifiers trained on each 10 ms time bin were also tested on every time bin from −350 to 800 ms following presentation of stimuli from held out data. The resulting accuracy image demonstrates temporal selectivity. Classifiers identify with good accuracy representations of stimuli specific to the timepoint on which they were trained. B – D Values reflect group means across 19 participants. A Scissor image was created by scarlab and published on svgrepo.com under an MIT license.
Lasso Regression, supplied by MathWorks Inc, used in various techniques. Bioz Stars score: 96/100, based on 1 PubMed citations. ZERO BIAS - scores, article reviews, protocol conditions and more
https://www.bioz.com/result/lasso regression/product/MathWorks Inc
Average 96 stars, based on 1 article reviews
lasso regression - by Bioz Stars, 2026-04
96/100 stars
  Buy from Supplier
function lassoglm  (MathWorks Inc)
90
MathWorks Inc function lassoglm
A Localizer Task. The Localizer Task was completed prior to the risky-decision task and to learning choice-outcome probabilities. On each trial participants were shown an outcome or choice stimulus, and, on the next screen, selected a word corresponding to the stimulus they had just observed. B Activation Probability measure. We trained <t>lasso-regularized</t> <t>logistic</t> <t>regression</t> classifiers to discriminate MEG data from when a given outcome stimulus was presented compared to data from presentation of all other images and inter-trial intervals. Each <t>classifier</t> output an estimated probability that its stimulus was being presented (Activation Probability). Separate classifiers were trained at successive 10 ms bins of MEG data around stimulus presentation. In the example, lines display the group-mean (+/− s.e.m.) activation probability measure for the classifier corresponding to O2, for each training timepoint, applied to held out data from the same corresponding test timepoint. Color designates the true outcome stimulus presented. C Decoding accuracy. Cross-validation accuracy is the proportion of trials for which the classifier corresponding to the presented outcome (for held-out data) had the highest activation probability. Lines denote mean accuracy (+/− s.e.m.) for each set of 10 ms time-binned outcome classifiers, applied to the same time-bin on held out examples. Dashed line designates permutation threshold corresponding to the 95 percentile peak threshold for accuracy lines generated with shuffled labels. D Temporal specificity. Classifiers trained on each 10 ms time bin were also tested on every time bin from −350 to 800 ms following presentation of stimuli from held out data. The resulting accuracy image demonstrates temporal selectivity. Classifiers identify with good accuracy representations of stimuli specific to the timepoint on which they were trained. B – D Values reflect group means across 19 participants. A Scissor image was created by scarlab and published on svgrepo.com under an MIT license.
Function Lassoglm, supplied by MathWorks 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/result/function lassoglm/product/MathWorks Inc
Average 90 stars, based on 1 article reviews
function lassoglm - by Bioz Stars, 2026-04
90/100 stars
  Buy from Supplier
lasso regularization  (MathWorks Inc)
90
MathWorks Inc lasso regularization
A Localizer Task. The Localizer Task was completed prior to the risky-decision task and to learning choice-outcome probabilities. On each trial participants were shown an outcome or choice stimulus, and, on the next screen, selected a word corresponding to the stimulus they had just observed. B Activation Probability measure. We trained <t>lasso-regularized</t> <t>logistic</t> <t>regression</t> classifiers to discriminate MEG data from when a given outcome stimulus was presented compared to data from presentation of all other images and inter-trial intervals. Each <t>classifier</t> output an estimated probability that its stimulus was being presented (Activation Probability). Separate classifiers were trained at successive 10 ms bins of MEG data around stimulus presentation. In the example, lines display the group-mean (+/− s.e.m.) activation probability measure for the classifier corresponding to O2, for each training timepoint, applied to held out data from the same corresponding test timepoint. Color designates the true outcome stimulus presented. C Decoding accuracy. Cross-validation accuracy is the proportion of trials for which the classifier corresponding to the presented outcome (for held-out data) had the highest activation probability. Lines denote mean accuracy (+/− s.e.m.) for each set of 10 ms time-binned outcome classifiers, applied to the same time-bin on held out examples. Dashed line designates permutation threshold corresponding to the 95 percentile peak threshold for accuracy lines generated with shuffled labels. D Temporal specificity. Classifiers trained on each 10 ms time bin were also tested on every time bin from −350 to 800 ms following presentation of stimuli from held out data. The resulting accuracy image demonstrates temporal selectivity. Classifiers identify with good accuracy representations of stimuli specific to the timepoint on which they were trained. B – D Values reflect group means across 19 participants. A Scissor image was created by scarlab and published on svgrepo.com under an MIT license.
Lasso Regularization, supplied by MathWorks 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/result/lasso regularization/product/MathWorks Inc
Average 90 stars, based on 1 article reviews
lasso regularization - by Bioz Stars, 2026-04
90/100 stars
  Buy from Supplier

Image Search Results


A Localizer Task. The Localizer Task was completed prior to the risky-decision task and to learning choice-outcome probabilities. On each trial participants were shown an outcome or choice stimulus, and, on the next screen, selected a word corresponding to the stimulus they had just observed. B Activation Probability measure. We trained lasso-regularized logistic regression classifiers to discriminate MEG data from when a given outcome stimulus was presented compared to data from presentation of all other images and inter-trial intervals. Each classifier output an estimated probability that its stimulus was being presented (Activation Probability). Separate classifiers were trained at successive 10 ms bins of MEG data around stimulus presentation. In the example, lines display the group-mean (+/− s.e.m.) activation probability measure for the classifier corresponding to O2, for each training timepoint, applied to held out data from the same corresponding test timepoint. Color designates the true outcome stimulus presented. C Decoding accuracy. Cross-validation accuracy is the proportion of trials for which the classifier corresponding to the presented outcome (for held-out data) had the highest activation probability. Lines denote mean accuracy (+/− s.e.m.) for each set of 10 ms time-binned outcome classifiers, applied to the same time-bin on held out examples. Dashed line designates permutation threshold corresponding to the 95 percentile peak threshold for accuracy lines generated with shuffled labels. D Temporal specificity. Classifiers trained on each 10 ms time bin were also tested on every time bin from −350 to 800 ms following presentation of stimuli from held out data. The resulting accuracy image demonstrates temporal selectivity. Classifiers identify with good accuracy representations of stimuli specific to the timepoint on which they were trained. B – D Values reflect group means across 19 participants. A Scissor image was created by scarlab and published on svgrepo.com under an MIT license.

Journal: Nature Communications

Article Title: Heuristics in risky decision-making relate to preferential representation of information

doi: 10.1038/s41467-024-48547-z

Figure Lengend Snippet: A Localizer Task. The Localizer Task was completed prior to the risky-decision task and to learning choice-outcome probabilities. On each trial participants were shown an outcome or choice stimulus, and, on the next screen, selected a word corresponding to the stimulus they had just observed. B Activation Probability measure. We trained lasso-regularized logistic regression classifiers to discriminate MEG data from when a given outcome stimulus was presented compared to data from presentation of all other images and inter-trial intervals. Each classifier output an estimated probability that its stimulus was being presented (Activation Probability). Separate classifiers were trained at successive 10 ms bins of MEG data around stimulus presentation. In the example, lines display the group-mean (+/− s.e.m.) activation probability measure for the classifier corresponding to O2, for each training timepoint, applied to held out data from the same corresponding test timepoint. Color designates the true outcome stimulus presented. C Decoding accuracy. Cross-validation accuracy is the proportion of trials for which the classifier corresponding to the presented outcome (for held-out data) had the highest activation probability. Lines denote mean accuracy (+/− s.e.m.) for each set of 10 ms time-binned outcome classifiers, applied to the same time-bin on held out examples. Dashed line designates permutation threshold corresponding to the 95 percentile peak threshold for accuracy lines generated with shuffled labels. D Temporal specificity. Classifiers trained on each 10 ms time bin were also tested on every time bin from −350 to 800 ms following presentation of stimuli from held out data. The resulting accuracy image demonstrates temporal selectivity. Classifiers identify with good accuracy representations of stimuli specific to the timepoint on which they were trained. B – D Values reflect group means across 19 participants. A Scissor image was created by scarlab and published on svgrepo.com under an MIT license.

Article Snippet: Following this, data from all sensors for a given timepoint was used as training examples to train a lasso logistic regression classifier (using matlab function lassoglm).

Techniques: Activation Assay, Biomarker Discovery, Generated