{"id":"W2983445208","doi":"10.1142/s0218213019500209","title":"Incremental Subclass Support Vector Machine","year":2019,"lang":"en","type":"article","venue":"International Journal of Artificial Intelligence Tools","topic":"Face and Expression Recognition","field":"Computer Science","cited_by":4,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Windsor","funders":"","keywords":"Support vector machine; Computer science; Decision boundary; Classifier (UML); Discriminative model; Artificial intelligence; Convex optimization; Linear classifier; Machine learning; Synthetic data; Regular polygon; Pattern recognition (psychology); Kernel method; Kernel (algebra); 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":[0.001008616,0.0007162191,0.001418033,0.001231653,0.0003785534,0.001153137,0.002191306,0.0006608227,0.002049484],"category_scores_gemma":[0.004618397,0.0003178965,0.0008574863,0.001118797,0.0004407182,0.002053159,0.001222686,0.001191985,0.001046682],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006359936,"about_ca_system_score_gemma":0.0010282,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002587199,"about_ca_topic_score_gemma":0.001961294,"domain_scores_codex":[0.9990981,0.0001437818,0.0000728481,0.0002007303,0.0003837946,0.000100865],"domain_scores_gemma":[0.9977304,0.0005821749,0.0001774079,0.0005001258,0.000903484,0.0001063605],"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":[0.0003024754,0.0001897576,0.005059911,0.000203539,0.0001045524,0.0001670853,0.0001552995,0.1206313,0.009750923,0.009633061,0.007792518,0.8460096],"study_design_scores_gemma":[0.00001073413,0.00006896502,0.00104089,0.00001265604,0.00002499478,0.0001077723,0.0000335488,0.9864456,0.003942329,0.005267594,0.003029863,0.00001508366],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.05972768,0.0008724722,0.9318672,0.0001761242,0.0001509039,0.0001418289,0.0003745872,0.003218082,0.003471079],"genre_scores_gemma":[0.6833689,0.0007414609,0.3070434,0.0002160569,0.0002103989,0.0002156479,0.002482181,0.0003582439,0.005363575],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.002587199,"threshold_uncertainty_score":0.006856203,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04618922746386503,"score_gpt":0.3136424866477004,"score_spread":0.2674532591838354,"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."}}