LEADER 05150nam 22006015 450 001 9910299353403321 005 20200705142211.0 010 $a3-319-98131-5 024 7 $a10.1007/978-3-319-98131-4 035 $a(CKB)4100000007181169 035 $a(MiAaPQ)EBC5609375 035 $a(DE-He213)978-3-319-98131-4 035 $a(PPN)232472750 035 $a(EXLCZ)994100000007181169 100 $a20181129d2018 u| 0 101 0 $aeng 135 $aurcnu|||||||| 181 $ctxt$2rdacontent 182 $cc$2rdamedia 183 $acr$2rdacarrier 200 10$aExplainable and Interpretable Models in Computer Vision and Machine Learning /$fedited by Hugo Jair Escalante, Sergio Escalera, Isabelle Guyon, Xavier Baró, Ya?mur Güçlütürk, Umut Güçlü, Marcel van Gerven 205 $a1st ed. 2018. 210 1$aCham :$cSpringer International Publishing :$cImprint: Springer,$d2018. 215 $a1 online resource (305 pages) 225 1 $aThe Springer Series on Challenges in Machine Learning,$x2520-131X 311 $a3-319-98130-7 327 $a1 Considerations for Evaluation and Generalization in Interpretable Machine Learning -- 2 Explanation Methods in Deep Learning: Users, Values, Concerns and Challenges -- 3 Learning Functional Causal Models with Generative Neural Networks -- 4 Learning Interpretable Rules for Multi-label Classification -- 5 Structuring Neural Networks for More Explainable Predictions -- 6 Generating Post-Hoc Rationales of Deep Visual Classification Decisions -- 7 Ensembling Visual Explanations -- 8 Explainable Deep Driving by Visualizing Causal Action -- 9 Psychology Meets Machine Learning: Interdisciplinary Perspectives on Algorithmic Job Candidate Screening -- 10 Multimodal Personality Trait Analysis for Explainable Modeling of Job Interview Decisions -- 11 On the Inherent Explainability of Pattern Theory-based Video Event Interpretations. . 330 $aThis book compiles leading research on the development of explainable and interpretable machine learning methods in the context of computer vision and machine learning. Research progress in computer vision and pattern recognition has led to a variety of modeling techniques with almost human-like performance. Although these models have obtained astounding results, they are limited in their explainability and interpretability: what is the rationale behind the decision made? what in the model structure explains its functioning? Hence, while good performance is a critical required characteristic for learning machines, explainability and interpretability capabilities are needed to take learning machines to the next step to include them in decision support systems involving human supervision. This book, written by leading international researchers, addresses key topics of explainability and interpretability, including the following: · Evaluation and Generalization in Interpretable Machine Learning · Explanation Methods in Deep Learning · Learning Functional Causal Models with Generative Neural Networks · Learning Interpreatable Rules for Multi-Label Classification · Structuring Neural Networks for More Explainable Predictions · Generating Post Hoc Rationales of Deep Visual Classification Decisions · Ensembling Visual Explanations · Explainable Deep Driving by Visualizing Causal Attention · Interdisciplinary Perspective on Algorithmic Job Candidate Search · Multimodal Personality Trait Analysis for Explainable Modeling of Job Interview Decisions · Inherent Explainability Pattern Theory-based Video Event Interpretations. 410 0$aThe Springer Series on Challenges in Machine Learning,$x2520-131X 606 $aArtificial intelligence 606 $aOptical data processing 606 $aPattern recognition 606 $aArtificial Intelligence$3https://scigraph.springernature.com/ontologies/product-market-codes/I21000 606 $aImage Processing and Computer Vision$3https://scigraph.springernature.com/ontologies/product-market-codes/I22021 606 $aPattern Recognition$3https://scigraph.springernature.com/ontologies/product-market-codes/I2203X 615 0$aArtificial intelligence. 615 0$aOptical data processing. 615 0$aPattern recognition. 615 14$aArtificial Intelligence. 615 24$aImage Processing and Computer Vision. 615 24$aPattern Recognition. 676 $a006.31 702 $aEscalante$b Hugo Jair$4edt$4http://id.loc.gov/vocabulary/relators/edt 702 $aEscalera$b Sergio$4edt$4http://id.loc.gov/vocabulary/relators/edt 702 $aGuyon$b Isabelle$4edt$4http://id.loc.gov/vocabulary/relators/edt 702 $aBaró$b Xavier$4edt$4http://id.loc.gov/vocabulary/relators/edt 702 $aGüçlütürk$b Ya?mur$4edt$4http://id.loc.gov/vocabulary/relators/edt 702 $aGüçlü$b Umut$4edt$4http://id.loc.gov/vocabulary/relators/edt 702 $avan Gerven$b Marcel$4edt$4http://id.loc.gov/vocabulary/relators/edt 906 $aBOOK 912 $a9910299353403321 996 $aExplainable and Interpretable Models in Computer Vision and Machine Learning$91969772 997 $aUNINA