{"id":"W2154793733","doi":"10.1109/icpr.2006.586","title":"Function Dot Product Kernels for Support Vector Machine","year":2006,"lang":"en","type":"article","venue":"","topic":"Face and Expression Recognition","field":"Computer Science","cited_by":12,"is_retracted":false,"has_abstract":true,"ca_institutions":"Concordia University","funders":"Natural Sciences and Engineering Research Council of Canada; Canada Research Chairs","keywords":"Dot product; Support vector machine; Computer science; Relevance vector machine; Product (mathematics); Function (biology); Artificial intelligence; Mathematics","routes":{"ca_aff":true,"ca_fund":true,"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":[0.001819667,0.001018834,0.001099663,0.001176966,0.0003332222,0.001435495,0.001031461,0.001017248,0.001922812],"category_scores_gemma":[0.007119325,0.0003805346,0.0007796814,0.001467185,0.0008132965,0.002381534,0.0008793146,0.001546554,0.001530746],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006097999,"about_ca_system_score_gemma":0.0006108426,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00114013,"about_ca_topic_score_gemma":0.0005429063,"domain_scores_codex":[0.9982203,0.0005342427,0.0001665606,0.0002596411,0.0006912975,0.0001280092],"domain_scores_gemma":[0.9969276,0.001017902,0.0002474991,0.0004939313,0.001208098,0.0001049673],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.0004037763,0.0001498114,0.001512191,0.0003620463,0.0001677178,0.00027337,0.0001439046,0.2529215,0.02421505,0.1372985,0.008390106,0.5741619],"study_design_scores_gemma":[0.00000888671,0.0000683001,0.0002701533,0.00001222131,0.00001375275,0.0001002466,0.00001014998,0.9726873,0.004962701,0.01720401,0.004640004,0.0000222629],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.00387073,0.0002786555,0.9948056,0.00005533595,0.0000410033,0.00001974174,0.00003696748,0.0004119942,0.0004798933],"genre_scores_gemma":[0.3814688,0.001305853,0.6093753,0.0001850149,0.0001935672,0.0002719558,0.0008649647,0.0003725663,0.005961948],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.001922812,"threshold_uncertainty_score":0.009623468,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01330877352359214,"score_gpt":0.2304237066098354,"score_spread":0.2171149330862432,"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."}}