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001			978-3-319-98131-4
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020			_a9783319981314 _9978-3-319-98131-4
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245	1	0	_aExplainable and Interpretable Models in Computer Vision and Machine Learning _h[electronic resource] / _cedited by Hugo Jair Escalante, Sergio Escalera, Isabelle Guyon, Xavier Baró, Yağmur Güçlütürk, Umut Güçlü, Marcel van Gerven.
250			_a1st ed. 2018.
264		1	_aCham : _bSpringer International Publishing : _bImprint: Springer, _c2018.
300			_aXVII, 299 p. 73 illus., 58 illus. in color. _bonline resource.
336			_atext _btxt _2rdacontent
337			_acomputer _bc _2rdamedia
338			_aonline resource _bcr _2rdacarrier
347			_atext file _bPDF _2rda
490	1		_aThe Springer Series on Challenges in Machine Learning, _x2520-131X
500			_aAcceso multiusuario
505	0		_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. .
520			_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.
541			_fUABC ; _cTemporal ; _d01/01/2021-12/31/2023.
650		0	_aArtificial intelligence.
650		0	_aOptical data processing.
650		0	_aPattern recognition.
650	1	4	_aArtificial Intelligence. _0https://scigraph.springernature.com/ontologies/product-market-codes/I21000
650	2	4	_aImage Processing and Computer Vision. _0https://scigraph.springernature.com/ontologies/product-market-codes/I22021
650	2	4	_aPattern Recognition. _0https://scigraph.springernature.com/ontologies/product-market-codes/I2203X
700	1		_aEscalante, Hugo Jair. _eeditor. _4edt _4http://id.loc.gov/vocabulary/relators/edt
700	1		_aEscalera, Sergio. _eeditor. _0(orcid)0000-0003-0617-8873 _1https://orcid.org/0000-0003-0617-8873 _4edt _4http://id.loc.gov/vocabulary/relators/edt
700	1		_aGuyon, Isabelle. _eeditor. _4edt _4http://id.loc.gov/vocabulary/relators/edt
700	1		_aBaró, Xavier. _eeditor. _4edt _4http://id.loc.gov/vocabulary/relators/edt
700	1		_aGüçlütürk, Yağmur. _eeditor. _4edt _4http://id.loc.gov/vocabulary/relators/edt
700	1		_aGüçlü, Umut. _eeditor. _4edt _4http://id.loc.gov/vocabulary/relators/edt
700	1		_avan Gerven, Marcel. _eeditor. _4edt _4http://id.loc.gov/vocabulary/relators/edt
710	2		_aSpringerLink (Online service)
773	0		_tSpringer Nature eBook
776	0	8	_iPrinted edition: _z9783319981307
776	0	8	_iPrinted edition: _z9783319981321
830		0	_aThe Springer Series on Challenges in Machine Learning, _x2520-131X
856	4	0	_zLibro electrónico _uhttp://148.231.10.114:2048/login?url=https://doi.org/10.1007/978-3-319-98131-4
912			_aZDB-2-SCS
912			_aZDB-2-SXCS
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999			_c242306 _d242305