{"id":"W1550219943","doi":"10.1007/978-3-642-16355-5_9","title":"Support Vector Machines for Inhabitant Identification in Smart Houses","year":2010,"lang":"en","type":"book-chapter","venue":"Lecture notes in computer science","topic":"Video Surveillance and Tracking Methods","field":"Computer Science","cited_by":13,"is_retracted":false,"has_abstract":false,"ca_institutions":"Université de Sherbrooke","funders":"","keywords":"Computer science; Support vector machine; Password; Identification (biology); Feature selection; Classifier (UML); Machine learning; Artificial intelligence; Data mining; Feature vector; Smart environment; Process (computing); Computer security; Internet of Things","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"],"consensus_categories":[],"category_scores_codex":[0.002810346,0.0004659557,0.000576258,0.0009905113,0.0002086203,0.0005987618,0.002856471,0.000388574,0.00001155752],"category_scores_gemma":[0.0004053993,0.0004276202,0.0001566744,0.0006206335,0.0004271208,0.0006606181,0.0005647095,0.0008399722,0.0000261992],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000161752,"about_ca_system_score_gemma":0.0005397835,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00004221831,"about_ca_topic_score_gemma":0.001058575,"domain_scores_codex":[0.9963459,0.00005580944,0.0007308464,0.001547033,0.0006741929,0.0006462466],"domain_scores_gemma":[0.9968569,0.0009749513,0.0003576908,0.001421741,0.0002698822,0.0001188095],"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":[0.0000160069,0.00006791349,0.003286657,0.0001143934,0.00001185496,0.00007018545,0.0009363069,0.001700805,0.00515383,0.01983073,0.00002158422,0.9687898],"study_design_scores_gemma":[0.001064214,0.0004735541,0.0374166,0.0005178266,0.00001682383,0.0001688108,2.078542e-7,0.230847,0.0262881,0.6944032,0.006777816,0.002025845],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.0008094885,0.0001624981,0.9929251,0.0009012928,0.003937099,0.0006128476,0.00001387857,0.000167095,0.0004707173],"genre_scores_gemma":[0.3273399,0.00002980386,0.6708118,0.0008877258,0.000546202,0.0000528126,0.00001538892,0.00005358404,0.0002627874],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.9667639,"threshold_uncertainty_score":0.9998176,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02392744587186622,"score_gpt":0.2921565550762632,"score_spread":0.268229109204397,"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."}}