{"id":"W2787980378","doi":"10.1016/j.patcog.2018.01.028","title":"<mml:math xmlns:mml=\"http://www.w3.org/1998/Math/MathML\" altimg=\"si3.gif\" overflow=\"scroll\"> <mml:mrow> <mml:msub> <mml:mi>ℓ</mml:mi> <mml:mrow> <mml:mn>2</mml:mn> <mml:mo>,</mml:mo> <mml:mn>1</mml:mn> </mml:mrow> </mml:msub> <mml:mspace width=\"0.16em\"/> <mml:mo>−</mml:mo> <mml:mspace width=\"0.16em\"/> <mml:msub> <mml:mi>ℓ</mml:mi> <mml:mn>1</mml:mn> </mml:msub> </mml:mrow> </mml:math> regularized nonlinear multi-task representation learning based cognitive performance prediction of Alzheimer’s disease","year":2018,"lang":"lv","type":"article","venue":"Pattern Recognition","topic":"Dementia and Cognitive Impairment Research","field":"Medicine","cited_by":36,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Alberta","funders":"China National Funds for Distinguished Young Scientists; Fundamental Research Funds for the Central Universities; Foundation for Innovative Research Groups of the National Natural Science Foundation of China; National Natural Science Foundation of China","keywords":"Artificial intelligence; Computer science; Neuroimaging; Machine learning; Regularization (linguistics); Feature (linguistics); Kernel (algebra); Cognition; Algorithm; Pattern recognition (psychology); Mathematics; Psychology","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":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.001143437,0.00257387,0.001258016,0.002149245,0.0007129024,0.004493574,0.004014013,0.002635673,0.6737874],"category_scores_gemma":[0.006914226,0.001351491,0.001411845,0.002765295,0.000549225,0.003785285,0.002879355,0.00269022,0.609139],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001920539,"about_ca_system_score_gemma":0.001504906,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01094937,"about_ca_topic_score_gemma":0.009781783,"domain_scores_codex":[0.9994191,0.00008721078,0.00007425131,0.0001024017,0.0002306957,0.00008637759],"domain_scores_gemma":[0.9977607,0.0006262843,0.000101989,0.0006174541,0.0007163355,0.0001772492],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0001598537,0.00004538959,0.0001691182,0.0003035553,0.00001871814,0.00005522372,0.00005746708,0.0008174335,0.001627308,0.006862214,0.9412382,0.04864544],"study_design_scores_gemma":[0.0001276312,0.0000302732,0.0008605815,0.000124535,0.0000145326,0.0001322013,0.00005798788,0.006456713,0.009107583,0.01174465,0.9712793,0.00006390058],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"software","genre_gemma":"empirical","genre_scores_codex":[0.001306181,0.000292733,0.1634775,0.002521969,0.001047916,0.0005307074,0.2722279,0.3788188,0.1797764],"genre_scores_gemma":[0.02013109,0.001089566,0.1221272,0.002189201,0.0004141353,0.001594334,0.3690807,0.2279517,0.255422],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.6737874,"threshold_uncertainty_score":0.4653027,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02840789866483921,"score_gpt":0.2726524418056274,"score_spread":0.2442445431407882,"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."}}