{"id":"W2367545638","doi":"","title":"Application of Machine Pattern Classification Based on Support Vector Machine with Particle Swarm Optimization","year":2010,"lang":"en","type":"article","venue":"Microcomputer applications","topic":"Advanced Algorithms and Applications","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Support vector machine; Computer science; Particle swarm optimization; Artificial intelligence; Structured support vector machine; Relevance vector machine; Radial basis function; Pattern recognition (psychology); Machine learning; Basis (linear algebra); Artificial neural network; Mathematics","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":[],"consensus_categories":[],"category_scores_codex":[0.00007877798,0.0002153156,0.0001805602,0.00009453171,0.0001226567,0.00003040973,0.0002711395,0.00008647243,0.00005171095],"category_scores_gemma":[5.81465e-7,0.0002031236,0.00005136021,0.0004540538,0.00007137624,0.0001018337,0.00002044896,0.0002435872,0.00006073595],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00003719651,"about_ca_system_score_gemma":0.00002300374,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001745537,"about_ca_topic_score_gemma":0.00003515673,"domain_scores_codex":[0.9989058,0.000009805236,0.0003536871,0.0003476828,0.0001691819,0.000213866],"domain_scores_gemma":[0.9989817,0.00005994031,0.0001197766,0.0006117238,0.000121831,0.0001050788],"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":[0.00001003068,0.0003643315,0.001408493,0.00005869901,0.00002315354,2.617071e-7,0.00005398729,0.6708602,0.07344483,0.002384295,0.0001330893,0.2512586],"study_design_scores_gemma":[0.0004397103,0.00004646461,0.003437139,0.000006304191,0.0000228649,0.000004339883,0.000005168981,0.9351332,0.02999534,0.00005983451,0.03063218,0.0002174722],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.003971902,0.00001429223,0.9937065,0.0003416258,0.00001887859,0.001006132,0.0001505817,0.0003685081,0.0004215533],"genre_scores_gemma":[0.7708822,0.000006564989,0.2270278,0.0001164614,0.00009375281,0.001278056,0.0005262246,0.00005203004,0.00001698667],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.7669103,"threshold_uncertainty_score":0.8283139,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.004561906242359539,"score_gpt":0.20942945939175,"score_spread":0.2048675531493905,"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."}}