{"id":"W1588637626","doi":"10.1007/978-3-540-24581-0_67","title":"On Using Prototype Reduction Schemes and Classifier Fusion Strategies to Optimize Kernel-Based Nonlinear Subspace Methods","year":2003,"lang":"en","type":"book-chapter","venue":"Lecture notes in computer science","topic":"Face and Expression Recognition","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":false,"ca_institutions":"Carleton University","funders":"","keywords":"Computer science; Subspace topology; Classifier (UML); Kernel method; Kernel (algebra); Artificial intelligence; Computation; Pattern recognition (psychology); Dimensionality reduction; Algorithm; Support vector machine; 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":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0009075766,0.000482126,0.0004242576,0.0008015797,0.0003298635,0.0007491573,0.0008709049,0.0003615637,0.00002205641],"category_scores_gemma":[0.0001017437,0.000416899,0.00008166291,0.0005949436,0.0003333909,0.000637762,0.0003890744,0.0006393227,0.00001513181],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001808262,"about_ca_system_score_gemma":0.0004911346,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001301652,"about_ca_topic_score_gemma":0.000005464024,"domain_scores_codex":[0.9969193,0.0001157752,0.0003707094,0.001467568,0.0006478094,0.0004788816],"domain_scores_gemma":[0.9982215,0.0002653206,0.0002167596,0.00081956,0.0002696257,0.0002072126],"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":[0.0001150831,0.00008754877,0.000006742234,0.0001161686,0.00001423689,0.00003627537,0.0007596015,0.2634833,0.02131049,0.00540657,0.0000687118,0.7085953],"study_design_scores_gemma":[0.0003997346,0.0005192236,0.00001024218,0.001025168,0.00001157534,0.00004819387,0.000001820444,0.9002966,0.04760833,0.04769273,0.001631393,0.0007550369],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.001583837,0.0001371845,0.9945094,0.0008665414,0.001192329,0.0008833459,0.000002575883,0.000112922,0.0007118653],"genre_scores_gemma":[0.006275231,0.00002041233,0.9922267,0.001166993,0.0001533275,0.00002180205,0.000003792599,0.00002964684,0.0001021017],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.7078403,"threshold_uncertainty_score":0.9998283,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04007738797591105,"score_gpt":0.3246768860616608,"score_spread":0.2845994980857498,"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."}}