{"id":"W4231283574","doi":"10.1017/cbo9781107298019.017","title":"Kernel Methods","year":2014,"lang":"en","type":"book-chapter","venue":"Cambridge University Press eBooks","topic":"Machine Learning and Data Classification","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo","funders":"","keywords":"Margin (machine learning); AdaBoost; Sample complexity; Computational complexity theory; Kernel (algebra); Computer science; Feature (linguistics); Feature vector; Kernel method; Support vector machine; Artificial intelligence; Space (punctuation); Time complexity; Base (topology); Mathematics; Algorithm; Theoretical computer science; Machine learning; Discrete mathematics","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":[0.0003656464,0.0002914935,0.0003173084,0.0001821271,0.0001862692,0.0001205493,0.001667248,0.0002944265,0.000003010089],"category_scores_gemma":[0.00002937432,0.0003321403,0.0001592516,0.00001012126,0.0001075123,0.0001125908,0.0008155981,0.0005372347,0.00007184251],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00009827303,"about_ca_system_score_gemma":0.00007952115,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00007678792,"about_ca_topic_score_gemma":5.321281e-7,"domain_scores_codex":[0.9985213,0.0001619522,0.0001657508,0.0006954451,0.0002323523,0.0002231503],"domain_scores_gemma":[0.9978719,0.0001567654,0.0002787944,0.001413665,0.000115583,0.0001633322],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"not_applicable","study_design_scores_codex":[0.000005640918,0.000002426657,6.618463e-7,0.00002777929,0.0000316737,0.00002340374,0.00001827912,0.000001812048,0.00002837058,0.9095335,0.02606391,0.06426254],"study_design_scores_gemma":[0.0001984746,0.0000297726,0.00002741006,0.00004779169,0.0000487157,0.00001256627,0.000001828906,0.005855208,0.00007430046,0.00005762518,0.9932959,0.0003503777],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"other","genre_gemma":"other","genre_scores_codex":[3.905469e-7,0.00003900235,0.4578184,0.00004130138,0.0001706383,0.00008150542,0.00001587369,0.0002143418,0.5416186],"genre_scores_gemma":[0.00009510539,0.00003764006,0.0360531,0.0001183601,0.0001124323,3.907768e-7,0.0000553221,0.00002456265,0.9635031],"genre_candidate":"other","genre_consensus":"other","teacher_disagreement_score":0.967232,"threshold_uncertainty_score":0.999913,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03148310683097667,"score_gpt":0.2560223325853946,"score_spread":0.224539225754418,"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."}}