{"id":"W4307743410","doi":"10.32920/21428679.v1","title":"Pattern Classification of Signals Using Fisher Kernels","year":2022,"lang":"en","type":"preprint","venue":"","topic":"Time Series Analysis and Forecasting","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"Toronto Metropolitan University","funders":"Natural Sciences and Engineering Research Council of Canada; Canada Research Chairs","keywords":"Fisher kernel; Outlier; Pattern recognition (psychology); Fisher information; Computer science; Recurrence plot; Artificial intelligence; Visualization; Support vector machine; Dimension (graph theory); Feature vector; Mathematics; Kernel method; Machine learning; Kernel Fisher discriminant analysis; Nonlinear system","routes":{"ca_aff":true,"ca_fund":true,"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":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0004060668,0.0001558241,0.000314276,0.0001364426,0.00008599407,0.0001485959,0.001090238,0.00008881,0.001820139],"category_scores_gemma":[0.00001580299,0.0001459444,0.0002178479,0.0002613333,0.00002454083,0.0001552078,0.002262677,0.000261006,0.000005478267],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00006490046,"about_ca_system_score_gemma":0.00008877544,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0005440408,"about_ca_topic_score_gemma":0.00001224935,"domain_scores_codex":[0.9983982,0.00008913451,0.0004694833,0.0004956129,0.0003798319,0.0001677018],"domain_scores_gemma":[0.9984535,0.00003792681,0.0005180248,0.0008426558,0.000106186,0.00004168095],"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":[0.00001175255,0.0004464596,0.03092066,0.0006580261,0.0008560896,0.00003127937,0.003963085,0.2759336,0.02134012,0.02053497,0.004502639,0.6408013],"study_design_scores_gemma":[0.00004508504,0.00001815666,0.00379083,0.00003170851,0.00002905364,0.000001877138,0.0001001447,0.9926909,0.0005495401,0.001440134,0.00110403,0.0001985967],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.04820028,0.00009039902,0.9432507,0.0003222772,0.0003004041,0.0001260094,0.00001070305,0.00007232181,0.007626881],"genre_scores_gemma":[0.9724284,0.000008561259,0.02616057,0.00009536474,0.00006421551,0.00001200144,0.00002109981,0.00001320519,0.001196573],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9242281,"threshold_uncertainty_score":0.9990923,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.08790420776321946,"score_gpt":0.2923725021050333,"score_spread":0.2044682943418138,"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."}}