Selected topics such as boosting, feature selection and multiclass classification will complete the theory part of the course.
A two-step boosting feature selection (BFS) technique is proposed for selecting a compact and discriminant user-specific feature representation from a large number of feature extractions.
The boosting feature is free to try, but afterward, it costs $2 for a 24-hour browsing blast.
As such, there may be value in boosting feature representations to ameliorate healthy (e.g., aging [ 40]) and clinical (e.g., attention-deficit/hyperactivity disorder [ 41]; neuropsychological patients [ 21]) populations who show impaired ability to ignore distracting information during everyday tasks.
These features will be called boosted and labeled as ({widehat{mathbf{X}}},) and the formalized training set containing only boosted features will be labeled as (widehat{G}^{l}).
Cumulative features (widehat{varvec{A}}^) are determined in accordance with the method of generating cumulative features based on boosted features (widehat{varvec{X}}^{l}). 3.
A set of boosted features (widehat{varvec{X}}^{l}) is generated for the formalized training set ({widehat{mathrm{G}}}^{l}) in accordance with the algorithm proposed above.
To identify the most significant (boosted) features, it is proposed to apply the Adaboost method that resulted in an approximately tenfold decrease in the dimensionality of the feature space.
the cumulative features are generated (widehat{varvec{A}}^{l_p }) based on boosted features (widehat{varvec{X}}^{l_p }) of the training set for each combination of gender ("Male", "Female") and race ("Caucasian", "Asian", "Black") groups (p; p= 1, 6);).
Stage 1 Generation of a decision function (f^{mathrm{gender}}(widehat{x})) for the binary gender classification the objects using SVM on the boosted features and the proposed bootstrapping procedure.
selection of half of a normalized face image and application of LBP to this half-image in order to generate a feature vector containing only uniform patterns; usage of the Adaboost method for the "gender" attribute to exclude insignificant features from the image feature vector (the sum of weight coefficients of the boosted features is equal to 0.95).
