{"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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002801804,0.0001045293,0.0001916395,0.00008626923,0.0003100779,0.000107465,0.0001925185,0.00009153913,0.000004375223],"category_scores_gemma":[0.0004758415,0.00007180334,0.00006412014,0.0001602147,0.0004633822,0.00001219159,0.0001370614,0.0003791833,3.048162e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000009641291,"about_ca_system_score_gemma":0.00006094435,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00003269098,"about_ca_topic_score_gemma":0.0003084563,"domain_scores_codex":[0.9984489,0.0002950504,0.000479295,0.0001853201,0.0003118789,0.000279539],"domain_scores_gemma":[0.9982665,0.0004054777,0.0002519186,0.0001475958,0.0008304417,0.00009810862],"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":[0.001438106,0.0001960398,0.009453016,0.00004207603,0.0002406067,0.00002870188,0.0003018878,0.03973585,0.03719003,0.0002819898,0.0006010208,0.9104907],"study_design_scores_gemma":[0.0001104105,0.002457497,0.002650248,0.0000850881,0.00004651469,0.0001874791,0.0002773218,0.9565722,0.03593531,0.00114999,0.0003590421,0.0001688445],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.5689293,0.001629564,0.4290309,0.0001616259,0.00004923786,0.0001248785,6.425775e-7,0.000005600887,0.00006828702],"genre_scores_gemma":[0.9976341,0.0009833493,0.0007791782,0.00005295254,0.0005163627,0.000001235797,0.00001126905,0.00001058218,0.000010985],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.9168364,"threshold_uncertainty_score":0.2928055,"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."}}