{"id":"W180164409","doi":"","title":"Reliable Recognition of Handwritten Digits Using A Cascade Ensemble Classifier System and Hybrid Features","year":2006,"lang":"en","type":"dissertation","venue":"Spectrum Research Repository (Concordia University)","topic":"Handwritten Text Recognition Techniques","field":"Computer Science","cited_by":16,"is_retracted":false,"has_abstract":true,"ca_institutions":"Concordia University","funders":"","keywords":"Word error rate; Pattern recognition (psychology); Computer science; Artificial intelligence; Classifier (UML); Discriminative model; MNIST database; Cascade; Dimensionality reduction; Feature extraction; Speech recognition; Linear discriminant analysis; Artificial neural network; Machine learning; Engineering","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":[0.0006733258,0.0004118373,0.0006694118,0.002196436,0.0007263178,0.0004692941,0.001057955,0.0005024182,0.000005015996],"category_scores_gemma":[0.00006029041,0.000472352,0.000226919,0.001368025,0.0002397732,0.001014975,0.0003212292,0.001136547,0.00001076524],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0008329816,"about_ca_system_score_gemma":0.0009240352,"about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01715391,"about_ca_topic_score_gemma":0.003088166,"domain_scores_codex":[0.9960199,0.0005558241,0.0005041852,0.001111048,0.001030457,0.0007785829],"domain_scores_gemma":[0.9973208,0.0002551247,0.0004848012,0.0007919064,0.0008670538,0.0002803325],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.003602174,0.001784297,0.0146091,0.01749728,0.002387614,0.02443609,0.003589065,0.00004987509,0.6660156,0.03947945,0.05427222,0.1722772],"study_design_scores_gemma":[0.0008857488,0.0005162427,0.008141045,0.002258156,0.0001460323,0.0006194097,0.001435673,0.001405035,0.9772562,0.002535652,0.003891272,0.0009095218],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8336548,0.0005293091,0.005107119,0.00006390589,0.0006521887,0.001033591,0.00004881526,0.0005067447,0.1584035],"genre_scores_gemma":[0.9690923,0.000184829,0.001811322,0.000004656252,0.0002286181,0.00001022537,0.000180982,0.00006122555,0.02842585],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.3112406,"threshold_uncertainty_score":0.9997728,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02824630313831518,"score_gpt":0.2673176914936217,"score_spread":0.2390713883553066,"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."}}