{"id":"W4414721029","doi":"10.1007/978-3-032-02725-2_7","title":"Sparse Least Square SVM in Primal via Nesterov Accelerated Alternating Directions Method of Multipliers","year":2025,"lang":"en","type":"book-chapter","venue":"Lecture notes in computer science","topic":"Blind Source Separation Techniques","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Guelph","funders":"","keywords":"Support vector machine; Quadratic programming; Regularization (linguistics); Least squares support vector machine; Lasso (programming language); Least-squares function approximation; Context (archaeology); Lagrange multiplier","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":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00144344,0.0005028256,0.0006902884,0.002005062,0.0001703328,0.0003745527,0.002762637,0.000368012,0.00001224343],"category_scores_gemma":[0.0001622036,0.0005087579,0.0001459723,0.001567269,0.0003500197,0.0007850219,0.00129204,0.001049649,0.000005040332],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003608668,"about_ca_system_score_gemma":0.0006626862,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001914922,"about_ca_topic_score_gemma":0.0004394526,"domain_scores_codex":[0.9962214,0.000153511,0.0008930476,0.001425694,0.0007825203,0.0005237858],"domain_scores_gemma":[0.9971046,0.0007135081,0.0005114254,0.001166525,0.0003942127,0.0001096664],"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":[0.00001042145,0.00007465113,0.0004142297,0.00006472307,0.00001510817,0.00003268215,0.002121148,0.06670565,0.001204904,0.009991091,0.00001314934,0.9193522],"study_design_scores_gemma":[0.0003968635,0.0001419638,0.0006632989,0.0008335964,0.00000826905,0.00004156191,8.316158e-7,0.9482653,0.02022409,0.02826765,0.0005491039,0.0006074853],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.00022012,0.000129021,0.9935018,0.000516162,0.0008023343,0.0006135348,0.000005040719,0.000284661,0.003927319],"genre_scores_gemma":[0.1500338,0.00002463906,0.8486995,0.0007988294,0.00009457592,0.00002494652,0.000006397903,0.00002623617,0.0002910272],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.9187447,"threshold_uncertainty_score":0.9997364,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02856076614534515,"score_gpt":0.309109726171413,"score_spread":0.2805489600260678,"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."}}