{"id":"W2964594890","doi":"10.24963/ijcai.2019/328","title":"Scalable Semi-Supervised SVM via Triply Stochastic Gradients","year":2019,"lang":"en","type":"article","venue":"","topic":"Machine Learning and ELM","field":"Computer Science","cited_by":12,"is_retracted":false,"has_abstract":true,"ca_institutions":"Western University","funders":"Government of Jiangsu Province; National Natural Science Foundation of China; National Science Foundation","keywords":"Scalability; Computer science; Support vector machine; Kernel (algebra); Convexity; Artificial intelligence; Classifier (UML); Machine learning; Supervised learning; Stochastic gradient descent; Data point; Scaling; Algorithm; Mathematics; 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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002136164,0.00109917,0.001745325,0.0007115144,0.0004855076,0.00112711,0.00238247,0.001566521,0.00143229],"category_scores_gemma":[0.006557297,0.0007125767,0.0008942368,0.0006933401,0.001028285,0.002140601,0.001896471,0.002098594,0.000830914],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0009208153,"about_ca_system_score_gemma":0.001786934,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003110367,"about_ca_topic_score_gemma":0.003273112,"domain_scores_codex":[0.998583,0.0004695031,0.00009059793,0.0003069545,0.0004255712,0.0001244604],"domain_scores_gemma":[0.9975067,0.001048904,0.0002805089,0.0004044431,0.000583678,0.0001758758],"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":[0.0001769205,0.0001360371,0.001249519,0.0001193234,0.00009594877,0.0001099261,0.0001060121,0.7681255,0.005207473,0.01710746,0.004899815,0.202666],"study_design_scores_gemma":[0.000003953437,0.000009322872,0.00002697758,0.000001495387,0.000001098867,0.000005979483,0.000001947206,0.9973052,0.0002420646,0.002299003,0.0001010738,0.000001963167],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.01271756,0.0001726629,0.9850551,0.0001822953,0.00003459438,0.00005901,0.00004969064,0.001217394,0.000511715],"genre_scores_gemma":[0.5622704,0.0002042308,0.4329155,0.0003434473,0.0001383018,0.0003041768,0.0006709856,0.0003633246,0.002789719],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.003110367,"threshold_uncertainty_score":0.01129729,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.007283525687693027,"score_gpt":0.2187148411239851,"score_spread":0.211431315436292,"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."}}