{"id":"W1928273700","doi":"10.1109/ijcnn.1992.226861","title":"A neural model for adaptive Karhunen Loeve transformation (KLT)","year":2003,"lang":"en","type":"article","venue":"","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":23,"is_retracted":false,"has_abstract":true,"ca_institutions":"Queen's University","funders":"","keywords":"Dimension (graph theory); Covariance matrix; Gradient descent; Eigenvalues and eigenvectors; Artificial intelligence; Learning rule; Artificial neural network; Rate of convergence; Computer science; Convergence (economics); Transformation (genetics); Stochastic gradient descent; Covariance; Hebbian theory; Mathematics; Algorithm; Matrix (chemical analysis); Sequence (biology); Principal component analysis; Key (lock); Statistics; Combinatorics","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":[0.0004306761,0.000482589,0.0008265573,0.0003759873,0.0003546241,0.001103604,0.001736944,0.001150497,0.004045898],"category_scores_gemma":[0.00129195,0.0003218849,0.0007308683,0.0006463102,0.0005857372,0.001524082,0.0008469857,0.001487959,0.002332417],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006720777,"about_ca_system_score_gemma":0.001177493,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004583257,"about_ca_topic_score_gemma":0.004944747,"domain_scores_codex":[0.9996988,0.00004756601,0.00001640865,0.00008601223,0.0001199986,0.00003126451],"domain_scores_gemma":[0.9997935,0.0000565865,0.00002428018,0.00002859742,0.00008370029,0.00001338598],"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":[0.00007634833,0.000080705,0.0006670054,0.0001982033,0.00007328021,0.0002335949,0.0001163943,0.5917109,0.0146548,0.1217559,0.006739568,0.2636933],"study_design_scores_gemma":[0.000006367015,0.00001578529,0.00009867584,0.000007996033,0.000006309549,0.00005894148,0.000004162153,0.9819756,0.0009761063,0.01331273,0.00352575,0.00001165217],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.001013703,0.000133934,0.9967006,0.00009027653,0.00005281515,0.00001842909,0.00004249098,0.000354922,0.001592783],"genre_scores_gemma":[0.2895598,0.0009889533,0.6851136,0.0003436237,0.0002093159,0.0004791798,0.0005849034,0.0004101228,0.0223104],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.004583257,"threshold_uncertainty_score":0.0135349,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04101221170945962,"score_gpt":0.2571821347134992,"score_spread":0.2161699230040396,"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."}}