{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0009487428,0.0005629146,0.001108656,0.0008830619,0.0003109954,0.0008534912,0.0006135298,0.0007528178,0.001136033],"category_scores_gemma":[0.002459889,0.0002628784,0.0006208201,0.001004302,0.0003057221,0.0007750416,0.0004004085,0.000741866,0.0004254342],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000293607,"about_ca_system_score_gemma":0.0004369029,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001682214,"about_ca_topic_score_gemma":0.000906812,"domain_scores_codex":[0.9991758,0.0002074635,0.00007209707,0.0001440383,0.0003617855,0.00003877335],"domain_scores_gemma":[0.9992447,0.0002896855,0.00006332146,0.00007964666,0.0003047685,0.00001783575],"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":[0.0001566935,0.0001585294,0.00265301,0.0002547164,0.0001875628,0.0001734348,0.00009495074,0.2233799,0.01259654,0.009924784,0.003187755,0.7472321],"study_design_scores_gemma":[0.000008109101,0.00004251723,0.0005303328,0.000006491563,0.00001452413,0.00004046307,0.000006611592,0.9936566,0.00295942,0.001556287,0.001172455,0.000006129857],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01374354,0.0004203996,0.9824326,0.00017922,0.000142948,0.00007463972,0.00002964999,0.0007471184,0.002229785],"genre_scores_gemma":[0.4821331,0.0005507423,0.5137511,0.000107618,0.0001578197,0.0001820516,0.0001765633,0.00007893914,0.00286205],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.001682214,"threshold_uncertainty_score":0.005017459,"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."}}