{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001459727,0.0006699759,0.0005426611,0.002106883,0.0002466537,0.001253647,0.0004811076,0.0007856389,0.0020856],"category_scores_gemma":[0.008330143,0.000207195,0.0008726553,0.001440015,0.0004568576,0.001614773,0.0005893031,0.0007157159,0.0008646507],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005774095,"about_ca_system_score_gemma":0.000448275,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003937402,"about_ca_topic_score_gemma":0.001746938,"domain_scores_codex":[0.9992913,0.0001581881,0.00006534239,0.0001596742,0.0002318547,0.00009357421],"domain_scores_gemma":[0.9978816,0.001048845,0.0002240087,0.0003072431,0.0004767341,0.00006156499],"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":[0.000615343,0.0001944716,0.009328434,0.0001662882,0.0001030592,0.000232342,0.0002932269,0.1632614,0.07157071,0.01300877,0.003052591,0.7381734],"study_design_scores_gemma":[0.000005695276,0.00004798779,0.005021474,0.00001147614,0.00001210374,0.00008322015,0.0000380743,0.978811,0.01152561,0.003680501,0.0007401686,0.00002270582],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.1424864,0.0002730876,0.8536383,0.000233285,0.00006427665,0.00005658395,0.0002846866,0.001479329,0.001484009],"genre_scores_gemma":[0.7752725,0.0003236092,0.221037,0.0000624012,0.00003520769,0.0000532299,0.0006577132,0.000129751,0.002428572],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.003937402,"threshold_uncertainty_score":0.007828951,"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."}}