{"id":"W2001392867","doi":"10.1016/j.patcog.2007.03.022","title":"A novel cascade ensemble classifier system with a high recognition performance on handwritten digits","year":2007,"lang":"en","type":"article","venue":"Pattern Recognition","topic":"Handwritten Text Recognition Techniques","field":"Computer Science","cited_by":91,"is_retracted":false,"has_abstract":false,"ca_institutions":"Concordia University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Pattern recognition (psychology); Word error rate; Cascade; Computer science; MNIST database; Artificial intelligence; Discriminative model; Numeral system; Classifier (UML); Cascading classifiers; Artificial neural network; Random subspace method; Speech recognition; Machine learning; Engineering","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":["metaepi_narrow","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0008928858,0.0004566033,0.000387697,0.00059193,0.0003094389,0.0003568937,0.000515948,0.000276997,0.00004582675],"category_scores_gemma":[0.00003661053,0.0004056196,0.0001135329,0.0006532858,0.00007419697,0.001295411,0.000107315,0.0004795279,0.001235692],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003080948,"about_ca_system_score_gemma":0.00006392319,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001502927,"about_ca_topic_score_gemma":0.0001343834,"domain_scores_codex":[0.9967919,0.00009111881,0.0006387745,0.0009387441,0.0007627274,0.0007767424],"domain_scores_gemma":[0.9980639,0.0002373642,0.0003713385,0.0005908761,0.0004729454,0.0002636089],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.0001182816,0.0002565061,0.0004217691,0.0001738395,0.00004535095,0.0000848579,0.0002055717,0.000002138477,0.004797028,0.0000631222,0.0002201037,0.9936115],"study_design_scores_gemma":[0.005333094,0.003209548,0.02908603,0.005125813,0.000126767,0.002496494,0.0004294879,0.01037858,0.939281,0.001118051,0.0009234186,0.002491682],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.3814839,0.000008340953,0.6102285,0.000147333,0.0002532277,0.000541031,0.00005292684,0.0009589997,0.006325759],"genre_scores_gemma":[0.9737805,0.00002750665,0.02422377,0.0009852804,0.0003183187,0.0002384467,0.0002486762,0.0000629112,0.0001145462],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9911197,"threshold_uncertainty_score":0.9998395,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03118144229899085,"score_gpt":0.2355363793179783,"score_spread":0.2043549370189875,"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."}}