{"id":"W1502653702","doi":"10.1007/978-3-642-13772-3_14","title":"A New SVM + NDA Model for Improved Classification and Recognition","year":2010,"lang":"en","type":"book-chapter","venue":"Lecture notes in computer science","topic":"Face and Expression Recognition","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Windsor","funders":"","keywords":"Support vector machine; Computer science; Decision boundary; Artificial intelligence; Linear discriminant analysis; Pattern recognition (psychology); Extension (predicate logic); Boundary (topology); Machine learning; Data mining; Mathematics","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":[0.0008047834,0.000684292,0.00129046,0.0005945666,0.0005253895,0.001130281,0.002200811,0.001272233,0.004774817],"category_scores_gemma":[0.001324901,0.0004509049,0.001061589,0.0006461725,0.0002388726,0.001800492,0.001099907,0.001654842,0.006344153],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005605905,"about_ca_system_score_gemma":0.0007692805,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004018438,"about_ca_topic_score_gemma":0.006323667,"domain_scores_codex":[0.9993405,0.0001095569,0.00005074166,0.0001737565,0.0002758946,0.00004954761],"domain_scores_gemma":[0.9991363,0.0001413924,0.00002886918,0.0001436862,0.0005029368,0.00004683482],"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":[0.0002402267,0.0002140083,0.00105813,0.0001470506,0.0001188854,0.00006654894,0.00003082918,0.05104847,0.03677544,0.00478487,0.01497536,0.8905403],"study_design_scores_gemma":[0.000006240098,0.00003810149,0.0003417541,0.00000705992,0.0000237767,0.00006542844,0.000004285645,0.9873601,0.005233939,0.001048885,0.00585859,0.00001194076],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.006881211,0.0008720025,0.9872367,0.0002378048,0.000523643,0.00004914733,0.0002840234,0.002391019,0.00152442],"genre_scores_gemma":[0.1834681,0.0008530549,0.7831333,0.000503402,0.0005081255,0.0003381131,0.002292447,0.0004723348,0.02843102],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.004774817,"threshold_uncertainty_score":0.01597333,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0378367155671324,"score_gpt":0.2588933518337797,"score_spread":0.2210566362666473,"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."}}