{"id":"W2096331884","doi":"10.5539/cis.v4n3p116","title":"Learning to Combine Kernels for Object Categorization","year":2011,"lang":"en","type":"article","venue":"Computer and Information Science","topic":"Advanced Image and Video Retrieval Techniques","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"National Natural Science Foundation of China","keywords":"Computer science; Artificial intelligence; Kernel (algebra); Pattern recognition (psychology); Robustness (evolution); Categorization; Contextual image classification; Multiple kernel learning; Machine learning; Image (mathematics); Kernel method; Support vector machine; Mathematics","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002239363,0.0009650196,0.001368908,0.002001214,0.0004410006,0.001076123,0.001404123,0.001162106,0.001144468],"category_scores_gemma":[0.005015231,0.0005650814,0.001147303,0.001962394,0.0007319825,0.002998193,0.001485307,0.001265898,0.0009867173],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007481716,"about_ca_system_score_gemma":0.0008339869,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001793512,"about_ca_topic_score_gemma":0.001800678,"domain_scores_codex":[0.9982206,0.0003516267,0.0001285712,0.0006154886,0.0005013082,0.0001823723],"domain_scores_gemma":[0.9984107,0.0004521097,0.0001872366,0.0003892869,0.0004891438,0.000071522],"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":[0.0001797218,0.0002870298,0.00257531,0.0001315254,0.0002443981,0.00006612667,0.0001429256,0.1443492,0.02325903,0.01078093,0.003138113,0.8148457],"study_design_scores_gemma":[0.00001222738,0.0000708341,0.000766614,0.00000623649,0.00003444007,0.00007107019,0.00003016358,0.9788859,0.006287715,0.01265089,0.0011643,0.00001966773],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01718451,0.000187518,0.981473,0.00004691246,0.00001857842,0.00003417501,0.00002628039,0.0006902913,0.0003385405],"genre_scores_gemma":[0.518596,0.0003361012,0.4778853,0.0001090628,0.00007795826,0.0001288101,0.0004703512,0.0002039742,0.00219248],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.002239363,"threshold_uncertainty_score":0.01184303,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0232266403487054,"score_gpt":0.2746451721311799,"score_spread":0.2514185317824745,"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."}}