{"id":"W4298118924","doi":"10.1587/transinf.2022edp7069","title":"Frank-Wolfe for Sign-Constrained Support Vector Machines","year":2022,"lang":"en","type":"article","venue":"IEICE Transactions on Information and Systems","topic":"Machine Learning and ELM","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Institute of Genetics; Japan Society for the Promotion of Science; Environmental Restoration and Conservation Agency","keywords":"Computer science; Sign (mathematics); Support vector machine; Generalization; Representation (politics); Domain (mathematical analysis); Function (biology); Feature (linguistics); Artificial intelligence; Sign function; Limit (mathematics); Mathematical optimization; Algorithm; Machine learning; Mathematics","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004861673,0.0001140469,0.0001412614,0.0002111901,0.0007497036,0.0002412121,0.0002386821,0.00002965642,0.0001113933],"category_scores_gemma":[0.00001023318,0.0001076091,0.00006654909,0.0002252841,0.00001706928,0.0007683361,0.000006900873,0.0001550162,0.00003435252],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0000358,"about_ca_system_score_gemma":0.00006176422,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001277565,"about_ca_topic_score_gemma":0.000004109036,"domain_scores_codex":[0.9990156,0.00007552067,0.0003451883,0.000132837,0.0002581041,0.000172806],"domain_scores_gemma":[0.9994112,0.0001179792,0.0001224438,0.0002119979,0.00005836067,0.00007806794],"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":[0.0001924614,0.0002685568,0.00009704068,0.0006093716,0.0001584905,0.000004621732,0.02221619,0.1979403,0.0001759678,0.08177757,0.009407541,0.6871518],"study_design_scores_gemma":[0.00103118,0.0004205605,0.0001766819,0.000009504109,0.00001093604,0.0001179895,0.0009131753,0.7382646,0.0000352648,0.00004895944,0.2587606,0.0002105106],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.002620177,0.00002410272,0.9907496,0.001089839,0.001136396,0.0004120513,0.0000989705,0.0002498245,0.003619062],"genre_scores_gemma":[0.9969412,0.000004775217,0.001140843,0.0005270218,0.00002977759,0.0002970151,0.00003091418,0.000005508737,0.001022923],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.994321,"threshold_uncertainty_score":0.576619,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01074248554767684,"score_gpt":0.2360208561674561,"score_spread":0.2252783706197792,"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."}}