explainable fuzzy concepts paradigms de kreinovich vladik (32 résultats)

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Hardback. Etat : New. 2022 ed. Modern AI techniques -- especially deep learning -- provide, in many cases, very good recommendations: where a self-driving car should go, whether to give a company a loan, etc. The problem is that not all these recommendations are good -- and since deep learning provides no explanations, we cannot tell which recommendations are good. It is therefore desirable to provide natural-language explanation of the numerical AI recommendations. The need to connect natural language rules and numerical decisions is known since 1960s, when the need emerged to incorporate expert knowledge -- described by imprecise words like "small" -- into control and decision making. For this incorporation, a special "fuzzy" technique was invented, that led to many successful applications. This book described how this technique can help to make AI more explainable.The book can be recommended for students, researchers, and practitioners interested in explainable AI.

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Etat : New. pp. 130 1st ed. 2022 edition NO-PA16APR2015-KAP.

Etat : New. pp. 140.

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Etat : New. pp. 130.

Etat : New. pp. 140.

Etat : As New. Unread book in perfect condition.

Etat : New. This is a Brand-new US Edition. This Item may be shipped from US or any other country as we have multiple locations worldwide.

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Etat : New. pp. 130.

Etat : New. pp. 140.

Paperback. Etat : new. Paperback. Modern AI techniques - especially deep learning - provide, in many cases, very good recommendations: where a self-driving car should go, whether to give a company a loan, etc. The problem is that not all these recommendations are good -- and since deep learning provides no explanations, we cannot tell which recommendations are good. It is therefore desirable to provide natural-language explanation of the numerical AI recommendations. The need to connect natural language rules and numerical decisions is known since 1960s, when the need emerged to incorporate expert knowledge -- described by imprecise words like "small" -- into control and decision making. For this incorporation, a special "fuzzy" technique was invented, that led to many successful applications. This book described how this technique can help to make AI more explainable.The book can be recommended for students, researchers, and practitioners interested in explainable AI. Modern AI techniques - especially deep learning - provide, in many cases, very good recommendations: where a self-driving car should go, whether to give a company a loan, etc. The problem is that not all these recommendations are good -- and since deep learning provides no explanations, we cannot tell which recommendations are good. Shipping may be from multiple locations in the US or from the UK, depending on stock availability.

Hardcover. Etat : new. Hardcover. Modern AI techniques - especially deep learning - provide, in many cases, very good recommendations: where a self-driving car should go, whether to give a company a loan, etc. The problem is that not all these recommendations are good -- and since deep learning provides no explanations, we cannot tell which recommendations are good. It is therefore desirable to provide natural-language explanation of the numerical AI recommendations. The need to connect natural language rules and numerical decisions is known since 1960s, when the need emerged to incorporate expert knowledge -- described by imprecise words like "small" -- into control and decision making. For this incorporation, a special "fuzzy" technique was invented, that led to many successful applications. This book described how this technique can help to make AI more explainable.The book can be recommended for students, researchers, and practitioners interested in explainable AI. Modern AI techniques - especially deep learning - provide, in many cases, very good recommendations: where a self-driving car should go, whether to give a company a loan, etc. The problem is that not all these recommendations are good -- and since deep learning provides no explanations, we cannot tell which recommendations are good. Shipping may be from multiple locations in the US or from the UK, depending on stock availability.

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Paperback. Etat : Brand New. 140 pages. 9.25x6.10x0.30 inches. In Stock.

Hardcover. Etat : Brand New. 140 pages. 9.25x6.10x0.38 inches. In Stock.

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Hardback. Etat : New. 1st ed. 2022.

Taschenbuch. Etat : Neu. Neuware -Modern AI techniques ¿- especially deep learning ¿- provide, in many cases, very good recommendations: where a self-driving car should go, whether to give a company a loan, etc. The problem is that not all these recommendations are good -- and since deep learning provides no explanations, we cannot tell which recommendations are good. It is therefore desirable to provide natural-language explanation of the numerical AI recommendations. The need to connect natural language rules and numerical decisions is known since 1960s, when the need emerged to incorporate expert knowledge -- described by imprecise words like 'small' -- into control and decision making. For this incorporation, a special 'fuzzy' technique was invented, that led to many successful applications. This book described how this technique can help to make AI more explainable.The book can be recommended for students, researchers, and practitioners interested in explainable AI.Springer Verlag GmbH, Tiergartenstr. 17, 69121 Heidelberg 140 pp. Englisch.

Buch. Etat : Neu. Neuware -Modern AI techniques ¿- especially deep learning ¿- provide, in many cases, very good recommendations: where a self-driving car should go, whether to give a company a loan, etc. The problem is that not all these recommendations are good -- and since deep learning provides no explanations, we cannot tell which recommendations are good. It is therefore desirable to provide natural-language explanation of the numerical AI recommendations. The need to connect natural language rules and numerical decisions is known since 1960s, when the need emerged to incorporate expert knowledge -- described by imprecise words like 'small' -- into control and decision making. For this incorporation, a special 'fuzzy' technique was invented, that led to many successful applications. This book described how this technique can help to make AI more explainable.The book can be recommended for students, researchers, and practitioners interested in explainable AI.Springer Verlag GmbH, Tiergartenstr. 17, 69121 Heidelberg 140 pp. Englisch.

Hardcover. Etat : new. Hardcover. Modern AI techniques - especially deep learning - provide, in many cases, very good recommendations: where a self-driving car should go, whether to give a company a loan, etc. The problem is that not all these recommendations are good -- and since deep learning provides no explanations, we cannot tell which recommendations are good. It is therefore desirable to provide natural-language explanation of the numerical AI recommendations. The need to connect natural language rules and numerical decisions is known since 1960s, when the need emerged to incorporate expert knowledge -- described by imprecise words like "small" -- into control and decision making. For this incorporation, a special "fuzzy" technique was invented, that led to many successful applications. This book described how this technique can help to make AI more explainable.The book can be recommended for students, researchers, and practitioners interested in explainable AI. Modern AI techniques - especially deep learning - provide, in many cases, very good recommendations: where a self-driving car should go, whether to give a company a loan, etc. The problem is that not all these recommendations are good -- and since deep learning provides no explanations, we cannot tell which recommendations are good. Shipping may be from our Sydney, NSW warehouse or from our UK or US warehouse, depending on stock availability.

Taschenbuch. Etat : Neu. Druck auf Anfrage Neuware - Printed after ordering - Modern AI techniques -- especially deep learning -- provide, inmany cases, very good recommendations: where a self-driving carshould go, whether to give a company a loan, etc. The problem isthat not all these recommendations are good -- and since deeplearning provides no explanations, we cannot tell whichrecommendations are good.It is therefore desirable to provide natural-language explanationof the numerical AI recommendations. The need to connect naturallanguage rules and numerical decisions is known since 1960s, whenthe need emerged to incorporate expert knowledge -- described byimprecise words like 'small' -- into control and decision making.For this incorporation, a special 'fuzzy' technique was invented,that led to many successful applications. This book described howthis technique can help to make AI more explainable.The book can be recommended for students, researchers, andpractitioners interested in explainable AI.

Buch. Etat : Neu. Druck auf Anfrage Neuware - Printed after ordering - Modern AI techniques -- especially deep learning -- provide, inmany cases, very good recommendations: where a self-driving carshould go, whether to give a company a loan, etc. The problem isthat not all these recommendations are good -- and since deeplearning provides no explanations, we cannot tell whichrecommendations are good.It is therefore desirable to provide natural-language explanationof the numerical AI recommendations. The need to connect naturallanguage rules and numerical decisions is known since 1960s, whenthe need emerged to incorporate expert knowledge -- described byimprecise words like 'small' -- into control and decision making.For this incorporation, a special 'fuzzy' technique was invented,that led to many successful applications. This book described howthis technique can help to make AI more explainable.The book can be recommended for students, researchers, andpractitioners interested in explainable AI.

Paperback. Etat : new. Paperback. Modern AI techniques - especially deep learning - provide, in many cases, very good recommendations: where a self-driving car should go, whether to give a company a loan, etc. The problem is that not all these recommendations are good -- and since deep learning provides no explanations, we cannot tell which recommendations are good. It is therefore desirable to provide natural-language explanation of the numerical AI recommendations. The need to connect natural language rules and numerical decisions is known since 1960s, when the need emerged to incorporate expert knowledge -- described by imprecise words like "small" -- into control and decision making. For this incorporation, a special "fuzzy" technique was invented, that led to many successful applications. This book described how this technique can help to make AI more explainable.The book can be recommended for students, researchers, and practitioners interested in explainable AI. Modern AI techniques - especially deep learning - provide, in many cases, very good recommendations: where a self-driving car should go, whether to give a company a loan, etc. The problem is that not all these recommendations are good -- and since deep learning provides no explanations, we cannot tell which recommendations are good. Shipping may be from our Sydney, NSW warehouse or from our UK or US warehouse, depending on stock availability.