{"id":"W2081990996","doi":"10.1109/tip.2013.2259836","title":"Cross-Domain Object Recognition Via Input-Output Kernel Analysis","year":2013,"lang":"en","type":"article","venue":"IEEE Transactions on Image Processing","topic":"Domain Adaptation and Few-Shot Learning","field":"Computer Science","cited_by":41,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of British Columbia","funders":"","keywords":"Kernel (algebra); Artificial intelligence; Reproducing kernel Hilbert space; Pattern recognition (psychology); Discriminative model; Kernel embedding of distributions; Computer science; Tree kernel; Domain (mathematical analysis); Radial basis function kernel; Kernel method; Benchmark (surveying); Feature vector; Feature (linguistics); Polynomial kernel; Cognitive neuroscience of visual object recognition; Support vector machine; Object (grammar); Mathematics; Hilbert space","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":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow","scholarly_communication","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0003610395,0.0002507356,0.0002623075,0.0006585551,0.0007232947,0.001488882,0.0004592577,0.0001070395,0.0005600911],"category_scores_gemma":[0.00001457772,0.0002526271,0.0002343009,0.002081087,0.0001202305,0.002728347,0.000004624293,0.0004086395,0.0008927458],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00009943559,"about_ca_system_score_gemma":0.00009459876,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001030905,"about_ca_topic_score_gemma":0.00001843386,"domain_scores_codex":[0.9979563,0.0001294606,0.0004311465,0.0006252618,0.0004352178,0.0004225843],"domain_scores_gemma":[0.9987687,0.00009874268,0.0001975822,0.0004014469,0.0003579952,0.0001755718],"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":[0.00001418261,0.0001738726,0.00005154559,0.00004072281,0.0001266791,0.000009827101,0.001935567,0.01019336,0.006694859,0.000009380116,0.00002896666,0.9807211],"study_design_scores_gemma":[0.0009752903,0.0001062105,0.003325003,0.00007938911,0.0001708605,0.00003760427,0.0003088521,0.9692375,0.0212462,0.003439127,0.0003525631,0.0007214199],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.0157913,0.00004271527,0.9800986,0.0003361185,0.0002388823,0.0002140945,0.00000420154,0.0004773136,0.002796774],"genre_scores_gemma":[0.8214713,0.000007514457,0.1768354,0.000477225,0.00003749453,0.00008862732,0.000005483274,0.00002268511,0.001054339],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.9799996,"threshold_uncertainty_score":0.9999926,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02139260948400089,"score_gpt":0.2733658145315424,"score_spread":0.2519732050475414,"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."}}