{"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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004818816,0.00007827827,0.00008235909,0.0002670281,0.0002896585,0.0003007475,0.0005127735,0.00002035229,0.000001913145],"category_scores_gemma":[0.00009290175,0.00006890346,0.00001748952,0.000808686,0.00007215546,0.01033472,0.0002843096,0.00005249777,0.00001842007],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002274015,"about_ca_system_score_gemma":0.00005254233,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000004476143,"about_ca_topic_score_gemma":8.675699e-8,"domain_scores_codex":[0.9992329,0.000008852158,0.0002005844,0.0001716693,0.0001910535,0.0001949751],"domain_scores_gemma":[0.9992629,0.00003030595,0.0000784911,0.0001821009,0.0003396835,0.0001064642],"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":[0.000009183756,0.00001292764,0.0003230937,0.00001861685,0.000001689208,2.516875e-7,0.009353677,0.0001167665,0.0005217399,0.1387255,0.0002941491,0.8506224],"study_design_scores_gemma":[0.0008046894,0.002185783,0.02797668,0.00005499237,0.000004843467,0.00003778937,0.0001839841,0.7273334,0.1599599,0.01894714,0.06185208,0.0006587729],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.003259956,0.000006295158,0.9940461,0.00007694351,0.000190329,0.0002627608,5.905866e-7,0.0002074688,0.001949543],"genre_scores_gemma":[0.6040899,0.00001884217,0.394872,0.0009508426,0.00002237583,0.00001739602,0.00000231297,0.000002028117,0.00002424147],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.8499637,"threshold_uncertainty_score":0.7492421,"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."}}