Bob 2.0-based training of the binary Logistic Regression model

This algorithm is a legacy one. The API has changed since its implementation. New versions and forks will need to be updated.

Algorithms have at least one input and one output. All algorithm endpoints are organized in groups. Groups are used by the platform to indicate which inputs and outputs are synchronized together. The first group is automatically synchronized with the channel defined by the block in which the algorithm is deployed.

Group: asv_real

Endpoint Name Data Format Nature
asv_real_scores elie_khoury/string_probe_scores/1 Input
asv_probe_id system/text/1 Input
classifier tutorial/linear_machine/1 Output

Group: pad

Endpoint Name Data Format Nature
pad_scores_class system/text/1 Input
pad_file_id system/text/1 Input
pad_scores system/float/1 Input

Group: asv_attack

Endpoint Name Data Format Nature
asv_attack_scores elie_khoury/string_probe_scores/1 Input
asv_attack_id system/text/1 Input

The code for this algorithm in Python
The ruler at 80 columns indicate suggested POSIX line breaks (for readability).
The editor will automatically enlarge to accomodate the entirety of your input
Use keyboard shortcuts for search/replace and faster editing. For example, use Ctrl-F (PC) or Cmd-F (Mac) to search through this box

This algorithm will run a Logistic Regression model [LR] for a binary classification problem using features as inputs.

The inputs take feature vectors as input and a text flag indicating if the data is a hit (it should be 'real') or a miss.



Updated Name Databases/Protocols Analyzers
pkorshunov/pkorshunov/isv-asv-pad-fusion-complete/1/asv_isv-pad_gmm-fusion_lr-pa avspoof/2@physicalaccess_verify_train,avspoof/2@physicalaccess_verification,avspoof/2@physicalaccess_verification_spoof,avspoof/2@physicalaccess_verify_train_spoof,avspoof/2@physicalaccess_antispoofing pkorshunov/spoof-score-fusion-roc_hist/1

This table shows the number of times this algorithm has been successfully run using the given environment. Note this does not provide sufficient information to evaluate if the algorithm will run when submitted to different conditions.

Terms of Service | Contact Information | BEAT platform version 2.2.1b0 | © Idiap Research Institute - 2013-2024