{"id":"W2157894025","doi":"10.1007/978-3-540-35488-8_23","title":"Information Gain, Correlation and Support Vector Machines","year":2008,"lang":"en","type":"book-chapter","venue":"Studies in fuzziness and soft computing","topic":"Face and Expression Recognition","field":"Computer Science","cited_by":100,"is_retracted":false,"has_abstract":false,"ca_institutions":"Canadian Imperial Bank of Commerce (Canada)","funders":"","keywords":"Feature selection; Benchmark (surveying); Correlation; Feature (linguistics); Information gain; Selection (genetic algorithm); Support vector machine; Artificial intelligence; Computer science; Set (abstract data type); Machine learning; Pattern recognition (psychology); Data mining; Mathematics; Geography","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":[],"consensus_categories":[],"category_scores_codex":[0.0002010172,0.0002480025,0.0003677763,0.0002119607,0.0002908773,0.00008128044,0.0001350334,0.0001416941,0.000003972386],"category_scores_gemma":[0.00007521021,0.0002185823,0.00003646479,0.00005803039,0.0001231544,0.0005277144,0.000502089,0.0002391628,0.00001538649],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0000301899,"about_ca_system_score_gemma":0.00002729461,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000007900734,"about_ca_topic_score_gemma":0.000005625243,"domain_scores_codex":[0.9989417,0.00001715955,0.0004162164,0.0002480905,0.000197749,0.0001790366],"domain_scores_gemma":[0.9991916,0.0002046672,0.0002389972,0.0001526507,0.0001681944,0.00004382679],"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":[0.00002330989,0.00001889379,0.002025875,0.0007387709,0.0001100831,0.00005466987,0.02165274,0.000574531,0.000004534765,0.02409191,0.01079703,0.9399077],"study_design_scores_gemma":[0.00579588,0.0007677382,0.02511485,0.008984322,0.0001643312,0.0009058221,0.002412766,0.6831523,0.00003344235,0.05344334,0.2142916,0.004933532],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"other","genre_gemma":"empirical","genre_scores_codex":[0.02989558,0.0405076,0.4127524,0.003212491,0.01416149,0.003207762,0.00009376434,0.001597549,0.4945714],"genre_scores_gemma":[0.9140159,0.03465223,0.01840728,0.002395755,0.001220391,0.00004745591,0.0003309099,0.0001014934,0.02882853],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9349741,"threshold_uncertainty_score":0.8913528,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03005900661737723,"score_gpt":0.2679775509179859,"score_spread":0.2379185443006087,"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."}}