{"id":"W4327850327","doi":"10.1007/978-981-19-6553-1_2","title":"Basics of SVM Method and Least Squares SVM","year":2023,"lang":"en","type":"book-chapter","venue":"Industrial and applied mathematics","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Waterloo","funders":"","keywords":"Karush–Kuhn–Tucker conditions; Support vector machine; Least squares support vector machine; Quadratic programming; Mathematical optimization; Computer science; Process (computing); Least-squares function approximation; Convex optimization; Regular polygon; Mathematics; Artificial intelligence","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":[0.0005978269,0.0007808216,0.0008580184,0.001476965,0.0004963069,0.001806462,0.001145471,0.001517757,0.01271817],"category_scores_gemma":[0.001481927,0.0004132202,0.0006703988,0.002150444,0.0009501016,0.00276171,0.0007176248,0.002629553,0.01205728],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004466403,"about_ca_system_score_gemma":0.0005605676,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0007028388,"about_ca_topic_score_gemma":0.0006027004,"domain_scores_codex":[0.9994224,0.00009197131,0.00004730076,0.0001428696,0.0002660397,0.00002946936],"domain_scores_gemma":[0.9996222,0.0001442677,0.0000220044,0.00004700341,0.0001459931,0.00001840568],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.00004686493,0.0000732604,0.0003891397,0.0007704679,0.00003940902,0.0001548963,0.0001952633,0.008030531,0.008878126,0.4349132,0.06161512,0.4848937],"study_design_scores_gemma":[0.00001099696,0.00008352082,0.0008852178,0.0001923616,0.00002366197,0.001067373,0.00005905705,0.08198726,0.004522074,0.4251155,0.4859922,0.00006084288],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.001208714,0.02078976,0.911841,0.001293193,0.002175733,0.00006861949,0.0004284385,0.0008834447,0.06131117],"genre_scores_gemma":[0.06232923,0.03540637,0.7764842,0.001643145,0.00581298,0.0004168241,0.001830013,0.0009443652,0.115133],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.01271817,"threshold_uncertainty_score":0.04254651,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.09253394175284425,"score_gpt":0.2708215093802372,"score_spread":0.178287567627393,"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."}}