{"id":"W1486141598","doi":"10.1007/978-3-642-13681-8_37","title":"High Dimensional versus Low Dimensional Chaos in MPEG-7 Feature Binding for Object Classification","year":2010,"lang":"en","type":"book-chapter","venue":"Lecture notes in computer science","topic":"Chaos control and synchronization","field":"Physics and Astronomy","cited_by":0,"is_retracted":false,"has_abstract":false,"ca_institutions":"Concordia University","funders":"","keywords":"Computer science; Support vector machine; Artificial intelligence; Chaotic; Pattern recognition (psychology); Feature vector; Feature (linguistics); Classifier (UML); Binary number; CHAOS (operating system); Computer vision; Feature extraction; 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":[0.0004767433,0.0003074964,0.0002917334,0.0006781992,0.0003185746,0.0007705152,0.000774327,0.000518965,0.004779716],"category_scores_gemma":[0.001212933,0.0001774358,0.0002806719,0.0008602861,0.0003960138,0.0009712031,0.0007401832,0.0005441277,0.001643723],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000481936,"about_ca_system_score_gemma":0.0004066184,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002376904,"about_ca_topic_score_gemma":0.003681612,"domain_scores_codex":[0.9997355,0.00003785372,0.00002012341,0.00002754765,0.0001377933,0.00004118108],"domain_scores_gemma":[0.9996181,0.0001347958,0.00001875597,0.0001050168,0.0000992734,0.00002410357],"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":[0.000552598,0.0001016039,0.001041584,0.0001192821,0.00002410564,0.0001306812,0.0001868371,0.01735737,0.1044842,0.06037927,0.01601026,0.7996123],"study_design_scores_gemma":[0.00004162097,0.0001935916,0.003345372,0.00008890265,0.00004636198,0.0003499705,0.0001515198,0.7111542,0.1741208,0.07446691,0.0359687,0.00007206204],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.06161272,0.0009379741,0.9225324,0.0003049411,0.0001954946,0.0001311543,0.0006365114,0.003876374,0.009772512],"genre_scores_gemma":[0.4059162,0.0007972982,0.5736597,0.00018881,0.0001098447,0.0002392974,0.001953971,0.0005622092,0.01657272],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.004779716,"threshold_uncertainty_score":0.01598978,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01331058126773727,"score_gpt":0.238998082801908,"score_spread":0.2256875015341707,"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."}}