Last edited by Taujinn
Friday, January 31, 2020 | History

7 edition of An Inductive Logic Programming Approach to Statistical Relational Learning (Frontiers in Artificial Intelligence and Applications, Vol. 148) (Frontiers in Artificial Intelligence and Applications) found in the catalog.

An Inductive Logic Programming Approach to Statistical Relational Learning (Frontiers in Artificial Intelligence and Applications, Vol. 148) (Frontiers in Artificial Intelligence and Applications)

  • 358 Want to read
  • 0 Currently reading

Published by IOS Press .
Written in English

    Subjects:
  • Artificial intelligence,
  • Computers,
  • Computers - General Information,
  • Computer Books: Languages,
  • Artificial Intelligence - General,
  • Programming - General

  • The Physical Object
    FormatHardcover
    Number of Pages256
    ID Numbers
    Open LibraryOL12317735M
    ISBN 101586036742
    ISBN 109781586036744

    Machine Learning Authors: Abdelhamid Mellouk and Abdennacer Chebira Year: Pages: pages Book Description: Machine Learning can be defined in various ways related to a scientific domain concerned with the design and development of theoretical and implementation tools that allow building systems with some Human Like intelligent behavior. Vennekens, Marc Denecker, and Maurice Bruynooghe. Parameter learning of logic programs for symbolic-statistical modeling. An explanation is a minimal set of probabilistic facts that is sufficient for entailing the query and a covering set of explanations is a set that contains all possible explanations for the query. Probabilistic logic programming.

    In: Demoen, B. Statistical relational learning builds on ideas from probability theory and statistics to address uncertainty while incorporating tools from logic, databases, and programming languages to represent structure. In: Dean, T. LNCS, vol. Gilks, W. Since the start, the problem of learning probabilistic logic programs has been the focus of much attention and a special issue of Theory and Practice of Logic Programming on Probability, Logic, and Learning has just appeared online.

    Hidden Markov Models HMMs are one of the most widely used machine learning technologies in Statistical Linguistics and Bioinformatics, and allow the representation of probabilistic finite automata. In the first, we are given the structure the rules of P and we just want to infer the parameters of P, while in the second we want to infer both the structure and the parameters of P. In this setting the parameters the probability values are fixed and the structure the rules are to be learned. It introduces representations, inference, and learning techniques for probability, logic, and their combinations. The probability of a ground query Q is then obtained from the joint distribution of the query and the worlds: it is the sum of the probability of worlds where the query is true.


Share this book
You might also like
Poverty and the transition to a marketeconomy in Mongolia

Poverty and the transition to a marketeconomy in Mongolia

Yesterdays with authors.

Yesterdays with authors.

Albury yesterday and today

Albury yesterday and today

Gods hall of mirrors

Gods hall of mirrors

First year building mathematics

First year building mathematics

Local government

Local government

Training your German shepherd dog

Training your German shepherd dog

Science

Science

Similarity and choice

Similarity and choice

The second family

The second family

Ginn mathematics.

Ginn mathematics.

The schools of medieval England.

The schools of medieval England.

The age of fable

The age of fable

Multiple world

Multiple world

Mental illness in nursing homes

Mental illness in nursing homes

Block grants and the intergovernmental system

Block grants and the intergovernmental system

Windows to the brain

Windows to the brain

An Inductive Logic Programming Approach to Statistical Relational Learning (Frontiers in Artificial Intelligence and Applications, Vol. 148) (Frontiers in Artificial Intelligence and Applications) by Kristian Kersting Download PDF Ebook

Statistical relational learning

In: Gottlob, G. Thon, I. In the following we provide an overview of PILP by concentrating on languages under the distribution semantics. Milch, B. Proceedings of the Conference on Intelligent User Interfaces pp. Many of these tools have common underpinnings but are An Inductive Logic Programming Approach to Statistical Relational Learning book expressed with different terminology.

The treatment is comprehensive and self-contained, targeted at researchers and students in machine learning and applied statistics. Uppsala, Sweden: ACL. Next Kristian investigates the approach of Learning from proofs. Williams Year: Pages: pages Book Description: Gaussian processes GPs An Inductive Logic Programming Approach to Statistical Relational Learning book a principled, practical, probabilistic approach to learning in kernel machines.

New Generation Computing, 11 3 —, It performs automatic discovery of process models expressed in a probabilistic logic. Structured Message Passingwith Vibhav Gogate. Hastie wrote much of the statistical modeling software in S-PLUS and invented principal curves and surfaces.

How can computers reason about and learn with complex data such as graphs and uncertain databases? Also, it introduces Bayesian logic programs and investigates the approach of Learning from proofs and the issue of upgrading Fisher Kernels to Relational Fisher kernels.

In the first, we are given the structure the rules of P and we just want to infer the parameters of P, while in the second we want to infer both the structure and the parameters of P. Statistical Predicate Inventionwith Stanley Kok. Springer, Can learned results be physically plausible or be made understandable by us?

Since then the field has steadily developed and many proposals for the integration of logic programming and probability have appeared. The EM algorithm is used to estimate the probability of models containing random variables that are not observed in the data. Jensen, D. Probabilistic logic programs and their semantics.

LNCS, vol. By presenting a variety of approaches, the book highlights commonalities and clarifies important differences among proposed approaches and, along the way, identifies important representational and algorithmic issues.

Santos Costa, V. This involves taking three disparate major areas of research and attempting a fusion among them. Madison, WI: Morgan Kaufmann. Probabilistic reasoning with answer sets.Although Inductive Logic Programming (ILP) is generally thought of as a research area at the intersection of machine learning and computational logic, Bergadano and Gunetti propose that most of the research in ILP has in fact come from machine learning, particularly in the evolution of inductive reasoning from pattern recognition, through initial approaches to symbolic machine learning, to.

Inductive logic programming is a new research area formed at the intersection of machine learning and logic programming. While the influence of logic programming has encouraged the development of strong theoretical foundations, this new area is inheriting its experimental orientation from machine learning.

Inductive Logic Programming will be an invaluable text for all students of computer. Next Kristian investigates the approach of Learning from proofs. This is an interesting new learning framework which is the first to go beyond the two standard semantic frameworks of Inductive Logic Programming.

Kristian then looks at the problem of upgrading HMMs to logical HMMs.Jan 29, pdf An Inductive Logic Programming Approach to Statistical Relational Learning pdf in Artificial Intelligence and Applications, Vol.

) [K. Kersting] on galisend.com *FREE* shipping on qualifying offers. In this publication, the author Kristian Kersting has made an assault on one of the hardest integration problems at the heart of Artificial Intelligence galisend.com by: Inductive logic programming (ILP) studies the learning of (Prolog) logic programs and other relational knowledge from examples.

Most machine learning algorithms are restricted to finite, propositional, feature-based representations of examples and concepts and cannot learn complex relational and recursive knowledge.Statistical Relational Learning (SRL), studies techniques that combine the ebook of relational learning (e.g.

inductive logic programming) and probabilistic learning (e.g. Bayesian networks). By combining the power of logic and probability, such systems can perform robust and accurate reasoning and learning about complex relational data.