{"id":"W2093275257","doi":"10.5430/air.v1n2p11","title":"Interpretable support vector regression","year":2012,"lang":"en","type":"article","venue":"Artificial Intelligence Research","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"European Social Fund; European Commission","keywords":"Support vector machine; Interpretability; Data mining; Fuzzy rule; Kernel (algebra); Identification (biology); Computer science; Artificial intelligence; Reduction (mathematics); Least squares support vector machine; Fuzzy logic; Kernel method; Relevance vector machine; Mathematics; Pattern recognition (psychology); Machine learning; Fuzzy set","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.001684941,0.0001060748,0.0001152442,0.0001489182,0.0003949552,0.000242632,0.001135399,0.00006691166,0.0004037855],"category_scores_gemma":[0.0001276866,0.00008419032,0.00005775788,0.001143204,0.0001442089,0.0007585134,0.0005468925,0.0004188086,0.002408668],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00005662956,"about_ca_system_score_gemma":0.00006741966,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00005881262,"about_ca_topic_score_gemma":0.00001102422,"domain_scores_codex":[0.9977807,0.0001587115,0.0002744504,0.0003309914,0.0005656034,0.0008895387],"domain_scores_gemma":[0.9984322,0.0003395638,0.00003938896,0.0006994067,0.0001985852,0.0002908363],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.000007921883,0.0001794049,0.0002294604,0.000004819693,0.000004598362,0.000003818598,0.0007213324,0.00003050921,0.01315778,0.6295424,0.008358221,0.3477598],"study_design_scores_gemma":[0.00002635032,0.0003228377,0.0005107309,0.00008541893,0.000004759536,0.00003962215,0.0006389922,0.09847603,0.629278,0.1663523,0.1037249,0.0005400056],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.03710278,0.0004162771,0.9335234,0.007239209,0.00105579,0.0005819623,0.000002915945,0.0002880874,0.01978954],"genre_scores_gemma":[0.9940833,0.00006124775,0.004285929,0.0001063351,0.000410286,0.00007129442,0.000002347504,0.000009722519,0.0009694782],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9569806,"threshold_uncertainty_score":0.9983681,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.2461539245253788,"score_gpt":0.4550938221825369,"score_spread":0.2089398976571581,"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."}}