{"id":"W2592420180","doi":"10.5220/0006256507380745","title":"Dynamic Selection of Exemplar-SVMs for Watch-list Screening through Domain Adaptation","year":2017,"lang":"en","type":"article","venue":"","topic":"Topic Modeling","field":"Computer Science","cited_by":10,"is_retracted":false,"has_abstract":false,"ca_institutions":"Polytechnique Montréal; Université du Québec","funders":"","keywords":"Domain adaptation; Support vector machine; Computer science; Selection (genetic algorithm); Artificial intelligence; Adaptation (eye); Machine learning; Domain (mathematical analysis); Pattern recognition (psychology); Mathematics; Classifier (UML); Psychology","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002292936,0.001890621,0.00245665,0.002923331,0.0009510267,0.001759751,0.003009293,0.002464879,0.004179516],"category_scores_gemma":[0.008859234,0.0005177976,0.001800637,0.001826794,0.0004767478,0.002590806,0.001918497,0.002830888,0.004303901],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006318363,"about_ca_system_score_gemma":0.001573952,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004955481,"about_ca_topic_score_gemma":0.007033766,"domain_scores_codex":[0.9981996,0.0005047749,0.0001348242,0.0005937464,0.0002791974,0.0002878269],"domain_scores_gemma":[0.9963164,0.001659027,0.000145636,0.0004071967,0.001137274,0.0003345334],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.001597295,0.001358678,0.01485841,0.0003595495,0.0003920683,0.0002937523,0.0002536461,0.06388982,0.02558779,0.002385461,0.0476351,0.8413883],"study_design_scores_gemma":[0.00005532401,0.000126643,0.001841046,0.00003108099,0.0001158828,0.0001143432,0.0001098274,0.9879743,0.005399155,0.002133246,0.002074324,0.00002482685],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.2030391,0.005003477,0.7684723,0.001400202,0.0008932776,0.0004600146,0.002209042,0.0139416,0.004580941],"genre_scores_gemma":[0.7887858,0.0008929035,0.1910033,0.0006890229,0.000586394,0.0004039293,0.01005122,0.0008785348,0.00670903],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.004955481,"threshold_uncertainty_score":0.01398182,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05166690286437465,"score_gpt":0.3054666274369633,"score_spread":0.2537997245725886,"validation_status":"score_only:v0-immature-baseline","note":"Baseline scores from an immature model (maturity gate not passed). Scores rank; they never assert a category."}}