{"id":"W2016084084","doi":"10.2478/jaiscr-2014-0021","title":"The Recognition Of Partially Occluded Objects with Support Vector Machines, Convolutional Neural Networks and Deep Belief Networks","year":2014,"lang":"en","type":"article","venue":"Journal of Artificial Intelligence and Soft Computing Research","topic":"Cell Image Analysis Techniques","field":"Biochemistry, Genetics and Molecular Biology","cited_by":42,"is_retracted":false,"has_abstract":true,"ca_institutions":"Concordia University","funders":"","keywords":"Artificial intelligence; Discriminative model; Deep belief network; Computer science; Convolutional neural network; Deep learning; Pattern recognition (psychology); Cognitive neuroscience of visual object recognition; Artificial neural network; Object (grammar); Generative grammar; Machine learning; Generative model; Support vector machine","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001273975,0.0006060757,0.0006251959,0.0007717332,0.0001932506,0.00104217,0.0006729782,0.0008932571,0.0009843666],"category_scores_gemma":[0.004079951,0.0004065971,0.0004798545,0.0008119546,0.0006279845,0.001516777,0.000743928,0.0008583704,0.000228065],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007544376,"about_ca_system_score_gemma":0.0005764394,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00523555,"about_ca_topic_score_gemma":0.00330045,"domain_scores_codex":[0.9995461,0.000120487,0.00002722041,0.0001016979,0.0001345602,0.00006999036],"domain_scores_gemma":[0.998107,0.001034092,0.0003208651,0.0001950173,0.0002486083,0.00009448425],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0005529618,0.0002210939,0.003973277,0.0001195246,0.00009392281,0.000169597,0.00009823633,0.6921223,0.02308803,0.006225498,0.001514225,0.2718213],"study_design_scores_gemma":[0.000002354088,0.00001469341,0.0002211183,0.000002295362,0.00000222568,0.000005544544,0.000004382149,0.9970265,0.001540848,0.00111923,0.00005803757,0.000002814607],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.3980449,0.001234251,0.5968788,0.0006044563,0.0001084833,0.00007527816,0.0002569997,0.001306392,0.001490311],"genre_scores_gemma":[0.8726647,0.0002113429,0.1257765,0.00007304539,0.00002815431,0.00004373886,0.0002414519,0.00002469335,0.0009363618],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.00523555,"threshold_uncertainty_score":0.01041013,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03376694866047653,"score_gpt":0.3291410184247963,"score_spread":0.2953740697643197,"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."}}