{"id":"W1873861864","doi":"10.1109/crv.2005.19","title":"Auto-Correlation Wavelet Support Vector Machine and Its Applications to Regression","year":2005,"lang":"en","type":"article","venue":"","topic":"Advanced Algorithms and Applications","field":"Engineering","cited_by":14,"is_retracted":false,"has_abstract":true,"ca_institutions":"McGill University","funders":"","keywords":"Pattern recognition (psychology); Wavelet; Kernel (algebra); Radial basis function kernel; Artificial intelligence; Mathematics; Support vector machine; Kernel method; Polynomial kernel; Gaussian function; Variable kernel density estimation; Autocorrelation; Discrete wavelet transform; Stationary wavelet transform; Cascade algorithm; Computer science; Wavelet transform; Gaussian; Statistics; Pure mathematics; Physics","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":[],"consensus_categories":[],"category_scores_codex":[0.00002863805,0.00008875561,0.00007129955,0.0000442089,0.00007484172,0.00001290319,0.00005182669,0.0000377807,0.0001988742],"category_scores_gemma":[0.000004640034,0.00007763103,0.00001382213,0.0001410228,0.00000497941,0.0001136021,0.0000198951,0.00007109836,0.0003035706],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00003132698,"about_ca_system_score_gemma":0.000004202247,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000001745807,"about_ca_topic_score_gemma":0.00001033585,"domain_scores_codex":[0.9995544,0.000002093847,0.0001259781,0.0001350526,0.00006324185,0.0001192113],"domain_scores_gemma":[0.9997164,0.0000197127,0.00001248445,0.000132257,0.00002122813,0.00009793448],"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":[0.000003476789,0.00005706588,0.00008014427,0.00003292498,0.0000158425,5.712623e-7,0.0002447888,0.04665659,0.0258887,0.03502595,0.007455747,0.8845382],"study_design_scores_gemma":[0.0001350895,0.0000118877,0.001611214,0.000006553836,0.000006242833,0.00000628568,0.00001178265,0.5326716,0.00554648,0.0002246472,0.4596105,0.000157721],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.006618561,0.0002398775,0.9806854,0.001362175,0.00004415507,0.0005288583,0.00003872011,0.0005725614,0.009909709],"genre_scores_gemma":[0.9330574,0.00009823043,0.06271657,0.0001586418,0.0001879994,0.000297177,0.00006292596,0.00002787784,0.003393184],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.9264388,"threshold_uncertainty_score":0.3901888,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.00799569349213427,"score_gpt":0.2507077244482054,"score_spread":0.2427120309560711,"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."}}