{"id":"W2002223291","doi":"10.1007/s11390-014-1415-z","title":"Minimizing the Discrepancy Between Source and Target Domains by Learning Adapting Components","year":2014,"lang":"en","type":"article","venue":"Journal of Computer Science and Technology","topic":"Domain Adaptation and Few-Shot Learning","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Waterloo; University of British Columbia","funders":"","keywords":"Computer science; Independence (probability theory); Embedding; Dimensionality reduction; Range (aeronautics); Feature (linguistics); Reproducing kernel Hilbert space; Domain (mathematical analysis); Theory of computation; Kernel (algebra); Domain adaptation; Feature vector; Curse of dimensionality; Artificial intelligence; Kernel method; Feature selection; Machine learning; Algorithm; Hilbert space; Support vector machine; 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":[0.00158054,0.001462571,0.00160206,0.001040463,0.0004562126,0.001055002,0.002042588,0.002179782,0.001226916],"category_scores_gemma":[0.007025308,0.000576623,0.0009092193,0.0009186984,0.0008106082,0.002370195,0.00213792,0.00226419,0.0007762149],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005874075,"about_ca_system_score_gemma":0.001181611,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003208378,"about_ca_topic_score_gemma":0.00355574,"domain_scores_codex":[0.9991247,0.0002540905,0.0000572506,0.0003340667,0.0001582275,0.0000716355],"domain_scores_gemma":[0.9969841,0.00196122,0.0001267307,0.0004187651,0.0004042967,0.0001048673],"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":[0.0007999003,0.0005034817,0.004864098,0.0004045874,0.0003531099,0.0001943054,0.0002391251,0.3487635,0.03526628,0.007231813,0.006699949,0.5946799],"study_design_scores_gemma":[0.00002462968,0.0000852772,0.0005500506,0.00001804581,0.00006024196,0.00009340998,0.00004991701,0.984762,0.005585084,0.008101689,0.000653144,0.00001646782],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.05301847,0.0007045948,0.9437262,0.0002296775,0.00006988006,0.00007009025,0.000086235,0.001173847,0.0009209003],"genre_scores_gemma":[0.6109488,0.0007209192,0.3818289,0.0005115847,0.0001377071,0.0001896661,0.001016115,0.0004279147,0.004218387],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.003208378,"threshold_uncertainty_score":0.008358777,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.009523472966358878,"score_gpt":0.2245756298638781,"score_spread":0.2150521568975192,"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."}}