Fabrizio Sebastiani's Books

Learning to Quantify

By: Andrea Esuli,Alessandro Fabris,Alejandro Moreo,Fabrizio Sebastiani

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Learning to Quantify

By: Andrea Esuli,Alessandro Fabris,Alejandro Moreo,Fabrizio Sebastiani

This open access book provides an introduction and an overview of learning to quantify (a.k.a. “quantification”), i.e. the task of training estimators of class proportions in unlabeled data by means of supervised learning. In data science, learning to quantify is a task of its own related to classification yet different from it, since estimating class proportions by simply classifying all data and counting the labels assigned by the classifier is known to often return inaccurate (“biased”) class proportion estimates. The book introduces learning to quantify by looking at the supervised learning methods that can be used to perform it, at the evaluation measures and evaluation protocols that should be used for evaluating the quality of the returned predictions, at the numerous fields of human activity in which the use of quantification techniques may provide improved results with respect to the naive use of classification techniques, and at advanced topics in quantification research. The book is suitable to researchers, data scientists, or PhD students, who want to come up to speed with the state of the art in learning to quantify, but also to researchers wishing to apply data science technologies to fields of human activity (e.g., the social sciences, political science, epidemiology, market research) which focus on aggregate (“macro”) data rather than on individual (“micro”) data.

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Page Count

145

Publisher

Springer Nature

Published Date

ISBN 10

3031204670

ISBN 13

9783031204678

Foundations of Probabilistic Logic Programming

Languages, Semantics, Inference and Learning

By: Fabrizio Riguzzi

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Foundations of Probabilistic Logic Programming

Languages, Semantics, Inference and Learning

By: Fabrizio Riguzzi

Since its birth, the field of Probabilistic Logic Programming has seen a steady increase of activity, with many proposals for languages and algorithms for inference and learning. This book aims at providing an overview of the field with a special emphasis on languages under the Distribution Semantics, one of the most influential approaches. The book presents the main ideas for semantics, inference, and learning and highlights connections between the methods. Many examples of the book include a link to a page of the web application http://cplint.eu where the code can be run online. This 2nd edition aims at reporting the most exciting novelties in the field since the publication of the 1st edition. The semantics for hybrid programs with function symbols was placed on a sound footing. Probabilistic Answer Set Programming gained a lot of interest together with the studies on the complexity of inference. Algorithms for solving the MPE and MAP tasks are now available. Inference for hybrid programs has changed dramatically with the introduction of Weighted Model Integration. With respect to learning, the first approaches for neuro-symbolic integration have appeared together with algorithms for learning the structure for hybrid programs. Moreover, given the cost of learning PLPs, various works proposed language restrictions to speed up learning and improve its scaling.

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Page Count

548

Publisher

CRC Press

Published Date

ISBN 10

1000923215

ISBN 13

9781000923216

Learning to Quantify

By: Andrea Esuli,Alessandro Fabris,Alejandro Moreo,Fabrizio Sebastiani

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Learning to Quantify

By: Andrea Esuli,Alessandro Fabris,Alejandro Moreo,Fabrizio Sebastiani

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145

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Springer Nature

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ISBN 10

3031204670

ISBN 13

9783031204678

Advances in Information Retrieval

25th European Conference on IR Research, ECIR 2003, Pisa, Italy, April 14-16, 2003, Proceedings

By: Fabrizio Sebastiani

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Advances in Information Retrieval

25th European Conference on IR Research, ECIR 2003, Pisa, Italy, April 14-16, 2003, Proceedings

By: Fabrizio Sebastiani

This book constitutes the refereed proceedings of the 25th European Conference on Information Retrieval Research, ECIR 2003, held in Pisa, Italy, in April 2003. The 31 revised full papers and 16 short papers presented together with two invited papers were carefully reviewed and selected from 101 submissions. The papers are organized in topical sections on IR and the Web; retrieval of structured documents; collaborative filtering and text mining; text representation and natural language processing; formal models and language models for IR; machine learning and IR; text categorization; usability, interactivity, and visualization; and architectural issues and efficiency.

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Page Count

640

Publisher

Springer Science & Business Media

Published Date

ISBN 10

3540012745

ISBN 13

9783540012740

Book's by Fabrizio Sebastiani

Learning to Quantify

Learning to Quantify

Foundations of Probabilistic Logic Programming

Foundations of Probabilistic Logic Programming

Learning to Quantify

Learning to Quantify

Advances in Information Retrieval

Advances in Information Retrieval

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