{"id":"W2170142494","doi":"10.1109/aero.2010.5446692","title":"Comparison of data reduction techniques based on the performance of SVM-type classifiers","year":2010,"lang":"en","type":"article","venue":"","topic":"Blind Source Separation Techniques","field":"Computer Science","cited_by":12,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"University of Toronto; Information Society Innovation Fund; Princeton University","keywords":"Dimensionality reduction; Principal component analysis; Reduction (mathematics); Computer science; Pattern recognition (psychology); Support vector machine; Data reduction; Artificial intelligence; Data mining; Data compression; Machine learning; Mathematics","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"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":[0.004212141,0.001324056,0.001272549,0.003028037,0.0004911539,0.001341796,0.0009321886,0.001132412,0.001484281],"category_scores_gemma":[0.01863543,0.0002265827,0.0008627076,0.001778089,0.0004219888,0.00186467,0.000708657,0.001120591,0.001423952],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000423972,"about_ca_system_score_gemma":0.0006033105,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001133855,"about_ca_topic_score_gemma":0.00142734,"domain_scores_codex":[0.9964922,0.0008202691,0.0004356909,0.0004313868,0.001593913,0.000226484],"domain_scores_gemma":[0.9884685,0.00644941,0.0006088057,0.001129339,0.003178846,0.0001651187],"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":[0.001602441,0.0006304788,0.006548937,0.0006482098,0.0004779494,0.0001110911,0.0001890612,0.05049198,0.04757107,0.001375378,0.003813426,0.88654],"study_design_scores_gemma":[0.0001747334,0.002100501,0.02346346,0.0001413689,0.0003291609,0.0006466769,0.0003724577,0.7950608,0.1678041,0.002633899,0.007106585,0.000166179],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.4416723,0.005733655,0.5390074,0.000847754,0.0006472741,0.0006002219,0.001117019,0.005582699,0.004791672],"genre_scores_gemma":[0.5497036,0.002019874,0.4415669,0.0001810165,0.0002607091,0.0003308791,0.002900043,0.000377638,0.002659382],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.004212141,"threshold_uncertainty_score":0.02227622,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.08838902822670726,"score_gpt":0.3614462267168739,"score_spread":0.2730571984901667,"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."}}