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Rules for building formal models that use fast-and-frugal heuristics, extending the psychological study of classification to the real world of uncertainty.
This book focuses on classification--allocating objects into categories--"in the wild," in real-world situations and far from the certainty of the lab. In the wild, unlike in typical psychological experiments, the future is not knowable and uncertainty cannot be meaningfully reduced to probability. Connecting the science of heuristics with machine learning, the book shows how to create formal models using classification rules that are simple, fast, and transparent and that can be as accurate as mathematically sophisticated algorithms developed for machine learning.
This book focuses on classification--allocating objects into categories--"in the wild," in real-world situations and far from the certainty of the lab. In the wild, unlike in typical psychological experiments, the future is not knowable and uncertainty cannot be meaningfully reduced to probability. Connecting the science of heuristics with machine learning, the book shows how to create formal models using classification rules that are simple, fast, and transparent and that can be as accurate as mathematically sophisticated algorithms developed for machine learning.
Rules for building formal models that use fast-and-frugal heuristics, extending the psychological study of classification to the real world of uncertainty.
This book focuses on classification--allocating objects into categories--"in the wild," in real-world situations and far from the certainty of the lab. In the wild, unlike in typical psychological experiments, the future is not knowable and uncertainty cannot be meaningfully reduced to probability. Connecting the science of heuristics with machine learning, the book shows how to create formal models using classification rules that are simple, fast, and transparent and that can be as accurate as mathematically sophisticated algorithms developed for machine learning.
This book focuses on classification--allocating objects into categories--"in the wild," in real-world situations and far from the certainty of the lab. In the wild, unlike in typical psychological experiments, the future is not knowable and uncertainty cannot be meaningfully reduced to probability. Connecting the science of heuristics with machine learning, the book shows how to create formal models using classification rules that are simple, fast, and transparent and that can be as accurate as mathematically sophisticated algorithms developed for machine learning.
Über den Autor
Konstantinos V. Katsikopoulos, Özgür Simsek, Marcus Buckmann, and Gerd Gigerenzer
Inhaltsverzeichnis
Acknowledgments ix
Introduction: Why Classification in the Wild? 1
1 Four Cases of Classification in the Wild 5
2 Fast-and-Frugal Classification 29
3 Building Fast-and-Frugal Classifiers 51
4 Classification in Machine Learning 79
5 Classification in Cognitive Psychology 109
6 Building a Safer World 129
7 Conclusions 149
Notes 151
Bibliography 171
Author Index 185
Subject Index 191
Introduction: Why Classification in the Wild? 1
1 Four Cases of Classification in the Wild 5
2 Fast-and-Frugal Classification 29
3 Building Fast-and-Frugal Classifiers 51
4 Classification in Machine Learning 79
5 Classification in Cognitive Psychology 109
6 Building a Safer World 129
7 Conclusions 149
Notes 151
Bibliography 171
Author Index 185
Subject Index 191
Details
Erscheinungsjahr: | 2021 |
---|---|
Fachbereich: | Erwachsenenbildung |
Produktart: | Schulbücher |
Rubrik: | Schule & Lernen |
Thema: | Volkshochschule |
Medium: | Buch |
Inhalt: | Einband - fest (Hardcover) |
ISBN-13: | 9780262045155 |
ISBN-10: | 026204515X |
Sprache: | Englisch |
Einband: | Gebunden |
Autor: |
Katsikopoulos, Konstantinos
Simsek, Ozgur |
Hersteller: | MIT Press Ltd |
Maße: | 236 x 196 x 20 mm |
Von/Mit: | Konstantinos Katsikopoulos (u. a.) |
Erscheinungsdatum: | 02.02.2021 |
Gewicht: | 0,49 kg |
Über den Autor
Konstantinos V. Katsikopoulos, Özgür Simsek, Marcus Buckmann, and Gerd Gigerenzer
Inhaltsverzeichnis
Acknowledgments ix
Introduction: Why Classification in the Wild? 1
1 Four Cases of Classification in the Wild 5
2 Fast-and-Frugal Classification 29
3 Building Fast-and-Frugal Classifiers 51
4 Classification in Machine Learning 79
5 Classification in Cognitive Psychology 109
6 Building a Safer World 129
7 Conclusions 149
Notes 151
Bibliography 171
Author Index 185
Subject Index 191
Introduction: Why Classification in the Wild? 1
1 Four Cases of Classification in the Wild 5
2 Fast-and-Frugal Classification 29
3 Building Fast-and-Frugal Classifiers 51
4 Classification in Machine Learning 79
5 Classification in Cognitive Psychology 109
6 Building a Safer World 129
7 Conclusions 149
Notes 151
Bibliography 171
Author Index 185
Subject Index 191
Details
Erscheinungsjahr: | 2021 |
---|---|
Fachbereich: | Erwachsenenbildung |
Produktart: | Schulbücher |
Rubrik: | Schule & Lernen |
Thema: | Volkshochschule |
Medium: | Buch |
Inhalt: | Einband - fest (Hardcover) |
ISBN-13: | 9780262045155 |
ISBN-10: | 026204515X |
Sprache: | Englisch |
Einband: | Gebunden |
Autor: |
Katsikopoulos, Konstantinos
Simsek, Ozgur |
Hersteller: | MIT Press Ltd |
Maße: | 236 x 196 x 20 mm |
Von/Mit: | Konstantinos Katsikopoulos (u. a.) |
Erscheinungsdatum: | 02.02.2021 |
Gewicht: | 0,49 kg |
Warnhinweis