Change of Representation and Inductive Bias, D. Paul Benjamin

Автор: James Cussens; Alessandra Russo
Название: Inductive Logic Programming
ISBN: 3319633414 ISBN-13(EAN): 9783319633411
Издательство: Springer
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Описание: This book constitutes the thoroughly refereed post-conference proceedings of the 26th International Conference on Inductive Logic Programming, ILP 2016, held in London, UK, in September 2016. The 10 full papers presented were carefully reviewed and selected from 29 submissions.

Автор: R. Festa
Название: Optimum Inductive Methods
ISBN: 0792324609 ISBN-13(EAN): 9780792324607
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Описание: This book deals with a basic problem arising within the Bayesian approach 1 to scientific methodology, namely the choice of prior probabilities. In Section 3, the methods used in TIP and BS for making multinomial inference~ are considered and some conceptual relationships between TIP and BS are pointed out.

Автор: D. Paul Benjamin
Название: Change of Representation and Inductive Bias
ISBN: 0792390555 ISBN-13(EAN): 9780792390558
Издательство: Springer
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Описание: Change of Representation and Inductive Bias One of the most important emerging concerns of machine learning researchers is the dependence of their learning programs on the underlying representations, especially on the languages used to describe hypotheses.

Автор: Paul E. Utgoff
Название: Machine Learning of Inductive Bias
ISBN: 0898382238 ISBN-13(EAN): 9780898382235
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Описание: This book is based on the author's Ph.D. dissertation 56]. The the- sis research was conducted while the author was a graduate student in the Department of Computer Science at Rutgers University. The book was pre- pared at the University of Massachusetts at Amherst where the author is currently an Assistant Professor in the Department of Computer and Infor- mation Science. Programs that learn concepts from examples are guided not only by the examples (and counterexamples) that they observe, but also by bias that determines which concept is to be considered as following best from the ob- servations. Selection of a concept represents an inductive leap because the concept then indicates the classification of instances that have not yet been observed by the learning program. Learning programs that make undesir- able inductive leaps do so due to undesirable bias. The research problem addressed here is to show how a learning program can learn a desirable inductive bias.

Автор: Paul E. Utgoff
Название: Machine Learning of Inductive Bias
ISBN: 1461294088 ISBN-13(EAN): 9781461294085
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Цена: 93160.00 T
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Описание: This book is based on the author's Ph.D. dissertation 56]. The the- sis research was conducted while the author was a graduate student in the Department of Computer Science at Rutgers University. The book was pre- pared at the University of Massachusetts at Amherst where the author is currently an Assistant Professor in the Department of Computer and Infor- mation Science. Programs that learn concepts from examples are guided not only by the examples (and counterexamples) that they observe, but also by bias that determines which concept is to be considered as following best from the ob- servations. Selection of a concept represents an inductive leap because the concept then indicates the classification of instances that have not yet been observed by the learning program. Learning programs that make undesir- able inductive leaps do so due to undesirable bias. The research problem addressed here is to show how a learning program can learn a desirable inductive bias.

Название: Inductive logic programming
ISBN: 3319237071 ISBN-13(EAN): 9783319237077
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Описание: This book constitutes the thoroughly refereed post-conference proceedings of the 24th International Conference on Inductive Logic Programming, ILP 2014, held in Nancy, France, in September 2014. The 14 revised papers presented were carefully reviewed and selected from 41 submissions.

Автор: Mill
Название: A System of Logic, Ratiocinative and Inductive
ISBN: 1108040896 ISBN-13(EAN): 9781108040891
Издательство: Cambridge Academ
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Описание: In this two-volume work of 1843, John Stuart Mill (1806-73) establishes the principles of inductive reasoning and experimental method that inform his later works of political and social philosophy. Volume 2 includes Book VI, `On the Logic of the Moral Sciences`, an important early treatment of social science.

Автор: Mill
Название: A System of Logic, Ratiocinative and Inductive
ISBN: 1108040888 ISBN-13(EAN): 9781108040884
Издательство: Cambridge Academ
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Описание: In this two-volume work of 1843, John Stuart Mill (1806-73) establishes the principles of inductive reasoning and experimental method that inform his later works of political and social philosophy. Volume 1 features his introduction, outlining the science of logic, and discussion of syllogisms and induction.

Автор: Inoue
Название: Inductive Logic Programming
ISBN: 3319405659 ISBN-13(EAN): 9783319405650
Издательство: Springer
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Описание: This book constitutes the thoroughly refereed post-conference proceedings of the 25th International Conference on Inductive Logic Programming, ILP 2015, held in Kyoto, Japan, in August 2015.The 14 revised papers presented were carefully reviewed and selected from 44 submissions. The papers focus on topics such as theories, algorithms, representations and languages, systems and applications of ILP, and cover all areas of learning in logic, relational learning, relational data mining, statistical relational learning, multi-relational data mining, relational reinforcement learning, graph mining, connections with other learning paradigms, among others.

Автор: Gerson Zaverucha; V?tor Santos Costa; Aline Paes
Название: Inductive Logic Programming
ISBN: 3662449226 ISBN-13(EAN): 9783662449226
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Описание: The conference now focuses on all aspects of learning in logic, multi-relational learning and data mining, statistical relational learning, graph and tree mining, relational reinforcement learning, and other forms of learning from structured data.

Автор: Theo A.F. Kuipers
Название: Studies in Inductive Probability and Rational Expectation
ISBN: 9400998325 ISBN-13(EAN): 9789400998322
Издательство: Springer
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Описание: 3 in philosophy, and therefore in metaphilosophy, cannot be based on rules that avoid spending time on pseudo-problems. They have to be as precise as possible and as simple as possible.

Автор: Sa?o D?eroski; Bart Goethals; Pan?e Panov
Название: Inductive Databases and Constraint-Based Data Mining
ISBN: 1489982175 ISBN-13(EAN): 9781489982179
Издательство: Springer
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Описание: This book is about inductive databases and constraint-based data mining, emerging research topics lying at the intersection of data mining and database research. The aim of the book as to provide an overview of the state-of- the art in this novel and - citing research area. Of special interest are the recent methods for constraint-based mining of global models for prediction and clustering, the uni?cation of pattern mining approaches through constraint programming, the clari?cation of the re- tionship between mining local patterns and global models, and the proposed in- grative frameworks and approaches for inducive databases. On the application side, applications to practically relevant problems from bioinformatics are presented. Inductive databases (IDBs) represent a database view on data mining and kno- edge discovery. IDBs contain not only data, but also generalizations (patterns and models) valid in the data. In an IDB, ordinary queries can be used to access and - nipulate data, while inductive queries can be used to generate (mine), manipulate, and apply patterns and models. In the IDB framework, patterns and models become ”?rst-class citizens” and KDD becomes an extended querying process in which both the data and the patterns/models that hold in the data are queried.