{"id":"W2094562516","doi":"10.1117/12.719407","title":"Classifier combination and feature selection methods for polarimetric SAR classification","year":2007,"lang":"en","type":"article","venue":"Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE","topic":"Synthetic Aperture Radar (SAR) Applications and Techniques","field":"Engineering","cited_by":7,"is_retracted":false,"has_abstract":true,"ca_institutions":"Defence Research and Development Canada; AUG Signals (Canada)","funders":"","keywords":"Computer science; Classifier (UML); Artificial intelligence; Pattern recognition (psychology); Boosting (machine learning); Feature selection; Margin classifier; Cascading classifiers; Feature vector; Synthetic aperture radar; Machine learning; Feature extraction; Quadratic classifier; Random subspace method","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.005616017,0.001217006,0.001754779,0.003212583,0.0006682621,0.00109632,0.001074942,0.001040864,0.001731708],"category_scores_gemma":[0.005675707,0.0006581873,0.00132185,0.00305741,0.0006168446,0.001498395,0.0007830014,0.001220039,0.001388625],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004932767,"about_ca_system_score_gemma":0.000612695,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001036426,"about_ca_topic_score_gemma":0.00139591,"domain_scores_codex":[0.9963175,0.001274141,0.0001983917,0.0005527097,0.001505526,0.000151785],"domain_scores_gemma":[0.9965777,0.001651479,0.0003365576,0.0004625131,0.000905472,0.00006622815],"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":[0.0002499995,0.0002411941,0.003660631,0.0001972737,0.0003798846,0.0001007237,0.0001088618,0.07819447,0.02726655,0.003262013,0.002620456,0.8837179],"study_design_scores_gemma":[0.00006801073,0.0005394259,0.005856616,0.00005345921,0.000318878,0.0005457928,0.00006759547,0.9337318,0.03792174,0.009492139,0.01129261,0.0001119032],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.01376856,0.001091755,0.9827844,0.0001467134,0.00007043684,0.0001072798,0.00005845816,0.0008866281,0.001085712],"genre_scores_gemma":[0.1912948,0.0007911976,0.804888,0.0001529018,0.0001946543,0.0003034355,0.0002919566,0.0001623743,0.001920681],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.005616017,"threshold_uncertainty_score":0.02970076,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01438906462178523,"score_gpt":0.2721523319815348,"score_spread":0.2577632673597495,"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."}}