{"id":"W1965164548","doi":"10.1007/s11432-012-4679-3","title":"Sparse kernel logistic regression based on L 1/2 regularization","year":2012,"lang":"en","type":"article","venue":"Science China Information Sciences","topic":"Sparse and Compressive Sensing Techniques","field":"Engineering","cited_by":25,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of British Columbia","funders":"National Natural Science Foundation of China","keywords":"Logistic regression; Kernel regression; Statistics; Kernel (algebra); Mathematics; Logistic model tree; Pattern recognition (psychology); Artificial intelligence; Regularization (linguistics); Regression; Computer science; Combinatorics","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001073372,0.000118547,0.00008410162,0.0004498072,0.0004857366,0.0002510793,0.0004117738,0.00004137932,0.0000284167],"category_scores_gemma":[0.0002295869,0.00008681696,0.000024018,0.001303837,0.0005913736,0.003638338,0.00003798078,0.00009035237,0.00009612089],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00007486441,"about_ca_system_score_gemma":0.00006729893,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000006602598,"about_ca_topic_score_gemma":2.913222e-7,"domain_scores_codex":[0.9985914,0.0000153487,0.0002083963,0.0001088649,0.0007135771,0.0003624563],"domain_scores_gemma":[0.9994739,0.00002994516,0.00008019367,0.0002328519,0.00007363914,0.0001094242],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.000021047,0.00009945693,0.01061853,0.00005451795,0.000004414309,0.000001074129,0.00383078,0.8002985,0.02263852,0.0571694,0.009422068,0.09584172],"study_design_scores_gemma":[0.00007440414,0.00004834824,0.01956959,0.00007847879,0.000002399764,0.000003844614,0.00009942157,0.9198526,0.05739629,0.001008316,0.001695154,0.0001712167],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.3906549,0.00007367572,0.382311,0.0003664314,0.002515063,0.0004292416,0.000006914158,0.001696187,0.2219465],"genre_scores_gemma":[0.9943048,0.000006003818,0.005385514,0.0002274604,0.00004591234,0.00000597049,0.000004417639,0.000003463857,0.00001640868],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.60365,"threshold_uncertainty_score":0.3735943,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03538238089486406,"score_gpt":0.2834943019113316,"score_spread":0.2481119210164675,"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."}}