{"id":"W1600049415","doi":"10.1007/978-3-642-13775-4_25","title":"Application of Wave Atoms Decomposition and Extreme Learning Machine for Fingerprint Classification","year":2010,"lang":"en","type":"book-chapter","venue":"Lecture notes in computer science","topic":"Biometric Identification and Security","field":"Computer Science","cited_by":15,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Windsor","funders":"","keywords":"Computer science; Extreme learning machine; Pattern recognition (psychology); Fingerprint (computing); Artificial intelligence; Dimensionality reduction; Principal component analysis; Decomposition; Curse of dimensionality; Feature (linguistics); Fingerprint recognition; Algorithm; Artificial neural network","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":[0.0009838843,0.0002307396,0.0002869172,0.0009243532,0.0002311771,0.0002296886,0.0008729019,0.0002895715,0.000004155262],"category_scores_gemma":[0.0001025547,0.0002277211,0.00007316906,0.0005623737,0.0003930764,0.0002837491,0.0003580394,0.0005170771,0.000003880426],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001031931,"about_ca_system_score_gemma":0.0001266506,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00002130118,"about_ca_topic_score_gemma":0.00003883247,"domain_scores_codex":[0.997901,0.00002387706,0.0004563484,0.0009406008,0.0004456854,0.0002325153],"domain_scores_gemma":[0.9980236,0.0003762495,0.0004682005,0.0006963161,0.0003478788,0.00008779866],"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":[0.000005008744,0.00002554348,0.0001212399,0.00005591159,0.000004443047,6.117742e-7,0.0003226246,0.0002155488,0.01107654,0.06256616,0.000001107615,0.9256052],"study_design_scores_gemma":[0.0001737146,0.00006668032,0.001613563,0.0000508209,0.000006577096,0.00001595767,1.237757e-7,0.902975,0.005955861,0.08686494,0.002039541,0.0002372056],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.0004473132,0.0002355463,0.9974904,0.0005513465,0.0004417603,0.0005116381,0.000006404761,0.00007191147,0.0002436971],"genre_scores_gemma":[0.6099883,0.00003763581,0.3896804,0.000101836,0.00008045266,0.00001886823,0.00002721313,0.00001149198,0.00005388429],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.9253681,"threshold_uncertainty_score":0.9286196,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03020231767106469,"score_gpt":0.2689959792688525,"score_spread":0.2387936615977878,"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."}}