{"id":"W2156352582","doi":"10.5194/isprsarchives-xl-1-w3-305-2013","title":"MAXIMUM MARGIN CLUSTERING OF HYPERSPECTRAL DATA","year":2013,"lang":"en","type":"article","venue":"The international archives of the photogrammetry, remote sensing and spatial information sciences/International archives of the photogrammetry, remote sensing and spatial information sciences","topic":"Remote-Sensing Image Classification","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Ottawa","funders":"","keywords":"Margin (machine learning); Cluster analysis; Hyperspectral imaging; Computer science; Support vector machine; Hyperplane; Artificial intelligence; Pattern recognition (psychology); Data mining; Machine learning; Mathematics","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":[0.001129899,0.001067811,0.001096413,0.002012848,0.0007511059,0.00107426,0.001058529,0.0009695044,0.00188331],"category_scores_gemma":[0.00269935,0.0003329044,0.0007826903,0.00182864,0.0006632304,0.001352428,0.0009378466,0.0009020168,0.00104269],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0008629836,"about_ca_system_score_gemma":0.0006598422,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002580574,"about_ca_topic_score_gemma":0.002295911,"domain_scores_codex":[0.9986073,0.0002798031,0.00008451766,0.0004074668,0.0005354792,0.00008548833],"domain_scores_gemma":[0.9988399,0.0002948106,0.0001434199,0.0002007914,0.0004809479,0.00004026582],"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":[0.0004921277,0.0002371816,0.00258792,0.0003540523,0.0001580062,0.000151141,0.0002612379,0.4439754,0.02877516,0.01006168,0.008478638,0.5044675],"study_design_scores_gemma":[0.000005296622,0.0000192167,0.0008197789,0.00001031279,0.000006271099,0.00002451608,0.00003352491,0.9882488,0.005820484,0.003799574,0.001199939,0.00001228718],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.03941207,0.0003561733,0.9564461,0.0001597748,0.00007613476,0.0000860014,0.0002456868,0.0009051947,0.002312851],"genre_scores_gemma":[0.4485424,0.000260692,0.5441306,0.0001256635,0.00007836673,0.0002055269,0.001877462,0.000286681,0.004492666],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.002580574,"threshold_uncertainty_score":0.006300271,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02478618484274863,"score_gpt":0.2503864576793579,"score_spread":0.2256002728366092,"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."}}